Summarize diet data for analysis
Max Lindmark
28 November, 2022
Intro
In this script, I take the collated stomach data set and calculate aggregates (feeding ratio, total weight of prey groups) and predictor variables for diet data, aggregate to get 1 stomach = 1 row per prey type (not prey individual). I also select only the columns I need for model fitting, join environmental covariates and cpue covariates for cod and flounder, and lastly saduria biomass densities.
Load packages & source functions
# Load libraries, install if needed
library(tidyverse)
#> Warning: package 'tidyr' was built under R version 4.0.5
library(readxl)
library(tidylog)
library(RCurl)
library(RColorBrewer)
#> Warning: package 'RColorBrewer' was built under R version 4.0.5
library(patchwork)
library(janitor)
library(forcats)
library(gapminder)
library(viridis)
library(ggridges)
library(raster)
library(icesDatras)
library(ggalluvial)
library(ggrepel)
library(ncdf4)
library(chron)
library(rnaturalearth)
library(rnaturalearthdata)
library(mapplots)
library(geosphere)
library(quantreg)
#> Warning in .recacheSubclasses(def@className, def, env): undefined subclass
#> "numericVector" of class "Mnumeric"; definition not updated
library(brms)
#> Warning: package 'Rcpp' was built under R version 4.0.5
library(sdmTMB)
options(mc.cores = parallel::detectCores())
world <- ne_countries(scale = "medium", returnclass = "sf")
# Source code for map plots
source("/Users/maxlindmark/Dropbox/Max work/R/cod_interactions/R/functions/map_plot.R")
# Load cache
# qwraps2::lazyload_cache_dir(path = "R/prepare_data/03_clean_stomach_data_cache/html")
theme_set(theme_plot())
# Continuous colors
options(ggplot2.continuous.colour = "viridis")
# Discrete colors
scale_colour_discrete <- function(...) {
scale_colour_brewer(palette = "Paired")
}
scale_fill_discrete <- function(...) {
scale_fill_brewer(palette = "Paired")
}Read data
d <- read_csv("data/clean/full_stomach_data.csv") %>%
dplyr::select(-...1)
#> New names:
#> Rows: 57730 Columns: 42
#> ── Column specification
#> ──────────────────────────────────────────────────────── Delimiter: "," chr
#> (18): prey_latin_name, comment, country, cruise, predator_code, stomach... dbl
#> (22): ...1, year, day, prey_length_cm, prey_weight_g, stage_digestion, ... lgl
#> (1): size_group_code date (1): date
#> ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
#> Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> • `` -> `...1`Plot data
head(data.frame(d))
#> year day prey_latin_name prey_length_cm prey_weight_g stage_digestion
#> 1 2015 NA Monoporeia affinis NA 0.01176923 1
#> 2 2015 NA Monoporeia affinis NA 0.01176923 1
#> 3 2015 NA Monoporeia affinis NA 0.01176923 1
#> 4 2015 NA Monoporeia affinis NA 0.01176923 1
#> 5 2015 NA Monoporeia affinis NA 0.01176923 1
#> 6 2015 NA Monoporeia affinis NA 0.01176923 1
#> comment quarter country cruise size_group_code pred_weight_g predator_code
#> 1 <NA> 4 SWE BITS NA 106.48 FLE
#> 2 <NA> 4 SWE BITS NA 106.48 FLE
#> 3 <NA> 4 SWE BITS NA 106.48 FLE
#> 4 <NA> 4 SWE BITS NA 106.48 FLE
#> 5 <NA> 4 SWE BITS NA 106.48 FLE
#> 6 <NA> 4 SWE BITS NA 106.48 FLE
#> stomach_state sample data_id pred_id date month haul validity
#> 1 <NA> 160 2015_4_FLE 2015_4_FLE_160 2015-11-21 11 8 V
#> 2 <NA> 160 2015_4_FLE 2015_4_FLE_160 2015-11-21 11 8 V
#> 3 <NA> 160 2015_4_FLE 2015_4_FLE_160 2015-11-21 11 8 V
#> 4 <NA> 160 2015_4_FLE 2015_4_FLE_160 2015-11-21 11 8 V
#> 5 <NA> 160 2015_4_FLE 2015_4_FLE_160 2015-11-21 11 8 V
#> 6 <NA> 160 2015_4_FLE 2015_4_FLE_160 2015-11-21 11 8 V
#> station_name subdiv weight specimen_note
#> 1 4 SE NORRA MIDSJ\xd6BANKEN 25 138 Mags\xe4ck sparad f\xf6r analys
#> 2 4 SE NORRA MIDSJ\xd6BANKEN 25 138 Mags\xe4ck sparad f\xf6r analys
#> 3 4 SE NORRA MIDSJ\xd6BANKEN 25 138 Mags\xe4ck sparad f\xf6r analys
#> 4 4 SE NORRA MIDSJ\xd6BANKEN 25 138 Mags\xe4ck sparad f\xf6r analys
#> 5 4 SE NORRA MIDSJ\xd6BANKEN 25 138 Mags\xe4ck sparad f\xf6r analys
#> 6 4 SE NORRA MIDSJ\xd6BANKEN 25 138 Mags\xe4ck sparad f\xf6r analys
#> species haul_id fle_kg_km2 lcod_kg_km2 scod_kg_km2 lat lon depth
#> 1 Flounder 2015_4_8 794.8598 5514.953 2626.168 56.06667 17.36667 41.6
#> 2 Flounder 2015_4_8 794.8598 5514.953 2626.168 56.06667 17.36667 41.6
#> 3 Flounder 2015_4_8 794.8598 5514.953 2626.168 56.06667 17.36667 41.6
#> 4 Flounder 2015_4_8 794.8598 5514.953 2626.168 56.06667 17.36667 41.6
#> 5 Flounder 2015_4_8 794.8598 5514.953 2626.168 56.06667 17.36667 41.6
#> 6 Flounder 2015_4_8 794.8598 5514.953 2626.168 56.06667 17.36667 41.6
#> prey_number_type prey_weight_type pred_length_cm
#> 1 un_aggregated_from_average pooled 22
#> 2 un_aggregated_from_average pooled 22
#> 3 un_aggregated_from_average pooled 22
#> 4 un_aggregated_from_average pooled 22
#> 5 un_aggregated_from_average pooled 22
#> 6 un_aggregated_from_average pooled 22
#> pred_weight_source ices_rect X Y predator_latin_name
#> 1 estimated_from_length 41G7 647.3339 6216.025 Platichthys flesus
#> 2 estimated_from_length 41G7 647.3339 6216.025 Platichthys flesus
#> 3 estimated_from_length 41G7 647.3339 6216.025 Platichthys flesus
#> 4 estimated_from_length 41G7 647.3339 6216.025 Platichthys flesus
#> 5 estimated_from_length 41G7 647.3339 6216.025 Platichthys flesus
#> 6 estimated_from_length 41G7 647.3339 6216.025 Platichthys flesus
plot_map_labels_fc +
geom_point(data = filter(d, species == "Cod"), aes(x = X*1000, y = Y*1000), size = 0.5) +
facet_grid(quarter~year) +
ggtitle("Cod")
#> filter: removed 34,111 rows (59%), 23,619 rows remaining
plot_map_labels_fc +
geom_point(data = filter(d, species == "Flounder"), aes(x = X*1000, y = Y*1000), size = 0.5) +
facet_grid(quarter~year) +
ggtitle("Flounder")
#> filter: removed 23,619 rows (41%), 34,111 rows remaining
Summarize and organize data
We want 1 row = 1 predator and the total weight for each present prey type
# Calculate total weight of prey by predator ID and prey species (i.e., across prey sizes). First create wide data frame so that I can sum easily across prey groups (columns)
d_wide <- d %>%
drop_na(prey_weight_g) %>%
group_by(pred_id, prey_latin_name) %>%
summarise(tot_prey_weight_g = sum(prey_weight_g)) %>%
ungroup() %>%
pivot_wider(names_from = prey_latin_name, values_from = tot_prey_weight_g) %>%
mutate_all(~ifelse(is.na(.), 0, .)) %>%
clean_names()
#> drop_na: removed 201 rows (<1%), 57,529 rows remaining
#> group_by: 2 grouping variables (pred_id, prey_latin_name)
#> summarise: now 11,088 rows and 3 columns, one group variable remaining (pred_id)
#> ungroup: no grouping variables
#> pivot_wider: reorganized (prey_latin_name, tot_prey_weight_g) into (Diastylis rathkei, Halicryptus spinulosus, Bylgides sarsi, NA, Clupeidae, …) [was 11088x3, now 5889x95]
#> mutate_all: changed 5,099 values (87%) of 'Diastylis rathkei' (5099 fewer NA)
#> changed 5,324 values (90%) of 'Halicryptus spinulosus' (5324 fewer NA)
#> changed 5,432 values (92%) of 'Bylgides sarsi' (5432 fewer NA)
#> changed 4,354 values (74%) of 'NA' (4354 fewer NA)
#> changed 5,655 values (96%) of 'Clupeidae' (5655 fewer NA)
#> changed 5,300 values (90%) of 'Sprattus sprattus' (5300 fewer NA)
#> changed 4,912 values (83%) of 'Mysis mixta' (4912 fewer NA)
#> changed 5,844 values (99%) of 'Stone' (5844 fewer NA)
#> changed 5,759 values (98%) of 'Crangon crangon' (5759 fewer NA)
#> changed 5,631 values (96%) of 'Gammarus sp.' (5631 fewer NA)
#> changed 5,794 values (98%) of 'Priapulida' (5794 fewer NA)
#> changed 5,808 values (99%) of 'Priapulus caudatus' (5808 fewer NA)
#> changed 5,748 values (98%) of 'Gasterosteus aculeatus' (5748 fewer NA)
#> changed 5,440 values (92%) of 'Pisces' (5440 fewer NA)
#> changed 4,942 values (84%) of 'Saduria entomon' (4942 fewer NA)
#> changed 5,784 values (98%) of 'Neomysis integer' (5784 fewer NA)
#> changed 5,589 values (95%) of 'Clupea harengus' (5589 fewer NA)
#> changed 5,600 values (95%) of 'Monoporeia affinis' (5600 fewer NA)
#> changed 5,877 values (>99%) of 'scales' (5877 fewer NA)
#> changed 5,888 values (>99%) of 'Waste' (5888 fewer NA)
#> changed 5,629 values (96%) of 'Gobiidae' (5629 fewer NA)
#> changed 5,331 values (91%) of 'Pontoporeia femorata' (5331 fewer NA)
#> changed 5,530 values (94%) of 'remains' (5530 fewer NA)
#> changed 5,858 values (99%) of 'Crustacea' (5858 fewer NA)
#> changed 5,881 values (>99%) of 'Mysidae' (5881 fewer NA)
#> changed 5,123 values (87%) of 'Limecola balthica' (5123 fewer NA)
#> changed 5,859 values (99%) of 'Bivalvia' (5859 fewer NA)
#> changed 5,888 values (>99%) of 'Halicryptus' (5888 fewer NA)
#> changed 5,886 values (>99%) of 'Zoarces viviparus' (5886 fewer NA)
#> changed 5,886 values (>99%) of 'Platichthys flesus' (5886 fewer NA)
#> changed 5,888 values (>99%) of 'digestive tract' (5888 fewer NA)
#> changed 5,887 values (>99%) of 'Wood' (5887 fewer NA)
#> changed 5,888 values (>99%) of 'Phyllodocida ' (5888 fewer NA)
#> changed 5,861 values (>99%) of 'Polychaeta' (5861 fewer NA)
#> changed 5,885 values (>99%) of 'Sand' (5885 fewer NA)
#> changed 5,746 values (98%) of 'Mytilus sp.' (5746 fewer NA)
#> changed 5,849 values (99%) of 'Amphipoda' (5849 fewer NA)
#> changed 5,888 values (>99%) of 'limecola balthica' (5888 fewer NA)
#> changed 5,847 values (99%) of 'Algae' (5847 fewer NA)
#> changed 5,888 values (>99%) of 'Pungitius pungitius' (5888 fewer NA)
#> changed 5,854 values (99%) of 'Scoloplos armiger' (5854 fewer NA)
#> changed 5,857 values (99%) of 'Gadus morhua' (5857 fewer NA)
#> changed 5,872 values (>99%) of 'priapulida' (5872 fewer NA)
#> changed 5,888 values (>99%) of 'Idotea sp.' (5888 fewer NA)
#> changed 5,878 values (>99%) of 'Enchelyopus cimbrius' (5878 fewer NA)
#> changed 5,888 values (>99%) of 'Pleuronectidae' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'Cumacea' (5888 fewer NA)
#> changed 5,887 values (>99%) of 'plastic' (5887 fewer NA)
#> changed 5,831 values (99%) of 'stone' (5831 fewer NA)
#> changed 5,885 values (>99%) of 'Crangon' (5885 fewer NA)
#> changed 5,888 values (>99%) of 'sprattus sprattus' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'Aglae' (5888 fewer NA)
#> changed 5,675 values (96%) of 'Macoma balthica' (5675 fewer NA)
#> changed 5,888 values (>99%) of 'Carbon' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'Gasterosteidae' (5888 fewer NA)
#> changed 5,863 values (>99%) of 'Mysida' (5863 fewer NA)
#> changed 5,888 values (>99%) of 'gobiidae' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'Gobius niger' (5888 fewer NA)
#> changed 5,887 values (>99%) of 'Palaemon sp.' (5887 fewer NA)
#> changed 5,886 values (>99%) of 'Mytilus sp' (5886 fewer NA)
#> changed 5,887 values (>99%) of 'Scales' (5887 fewer NA)
#> changed 5,886 values (>99%) of 'Ammodytidae' (5886 fewer NA)
#> changed 5,888 values (>99%) of 'Pectinaria sp.' (5888 fewer NA)
#> changed 5,879 values (>99%) of 'sand' (5879 fewer NA)
#> changed 5,774 values (98%) of 'Pontoporeiidae' (5774 fewer NA)
#> changed 5,887 values (>99%) of 'Mucus' (5887 fewer NA)
#> changed 5,888 values (>99%) of 'Pontoporeia femotara' (5888 fewer NA)
#> changed 5,881 values (>99%) of 'Remains' (5881 fewer NA)
#> changed 5,887 values (>99%) of 'mucus' (5887 fewer NA)
#> changed 5,888 values (>99%) of 'Priapulus' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'carbon' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'wood' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'halicryptus spinulosus' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'pisces' (5888 fewer NA)
#> changed 5,874 values (>99%) of 'Mya arenaria' (5874 fewer NA)
#> changed 5,888 values (>99%) of 'Gastrosacus' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'Nephtys ciliata' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'Litter/plastics' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'clupeidae' (5888 fewer NA)
#> changed 5,877 values (>99%) of 'Pontoporeidae' (5877 fewer NA)
#> changed 5,887 values (>99%) of 'Decapoda' (5887 fewer NA)
#> changed 5,874 values (>99%) of 'Praunus flexuosus' (5874 fewer NA)
#> changed 5,888 values (>99%) of 'Neogobius melanostomus' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'Plastics' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'Agonus cataphractus' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'clupea harengus' (5888 fewer NA)
#> changed 5,885 values (>99%) of 'Copepoda' (5885 fewer NA)
#> changed 5,873 values (>99%) of 'Halicryptus spinolusus' (5873 fewer NA)
#> changed 5,888 values (>99%) of 'Prapulida' (5888 fewer NA)
#> changed 5,722 values (97%) of 'Mytilus edulis' (5722 fewer NA)
#> changed 5,888 values (>99%) of 'Hydrobia sp.' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'Myoxocephalus scorpius' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'Idotea balthica' (5888 fewer NA)
#> changed 5,888 values (>99%) of 'Caridea' (5888 fewer NA)
# There is now a NA column. But it doesn't matter really, it's just the empty stomachs but these will be empty anyway because all other columns are empty!
str(d_wide)
#> tibble [5,889 × 95] (S3: tbl_df/tbl/data.frame)
#> $ pred_id : chr [1:5889] "2015_4_COD_1" "2015_4_COD_101" "2015_4_COD_103" "2015_4_COD_104" ...
#> $ diastylis_rathkei : num [1:5889] 0.03 0.06 0.14 0 0 ...
#> $ halicryptus_spinulosus : num [1:5889] 0 0.07 0.28 0.01 0 0 0 0 0.02 0 ...
#> $ bylgides_sarsi : num [1:5889] 0 0 0.03 0 0 0 0 0 0.42 0 ...
#> $ na : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ clupeidae : num [1:5889] 0 0 0 0 0 0 0 1.74 0 0 ...
#> $ sprattus_sprattus : num [1:5889] 0 0 0 0 0 0 0 51.1 0 0 ...
#> $ mysis_mixta : num [1:5889] 0 0 0 0 0 0 0 0 0.04 0 ...
#> $ stone : num [1:5889] 0 0 0 0 0 0 0 0 0.02 0 ...
#> $ crangon_crangon : num [1:5889] 0 0 0 0 0 0 0 0 0 0.05 ...
#> $ gammarus_sp : num [1:5889] 0 0 0 0 0 0 0 0 0 0.04 ...
#> $ priapulida : num [1:5889] 0 0 0 0 0 0 0 0 0 0.09 ...
#> $ priapulus_caudatus : num [1:5889] 0 0 0 0 0 0 0 0 0 0.14 ...
#> $ gasterosteus_aculeatus : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ pisces : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ saduria_entomon : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ neomysis_integer : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ clupea_harengus : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ monoporeia_affinis : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ scales : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ waste : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ gobiidae : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ pontoporeia_femorata : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ remains : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ crustacea : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ mysidae : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ limecola_balthica : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ bivalvia : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ halicryptus : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ zoarces_viviparus : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ platichthys_flesus : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ digestive_tract : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ wood : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ phyllodocida : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ polychaeta : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ sand : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ mytilus_sp : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ amphipoda : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ limecola_balthica_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ algae : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ pungitius_pungitius : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ scoloplos_armiger : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ gadus_morhua : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ priapulida_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ idotea_sp : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ enchelyopus_cimbrius : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ pleuronectidae : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ cumacea : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ plastic : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ stone_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ crangon : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ sprattus_sprattus_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ aglae : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ macoma_balthica : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ carbon : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ gasterosteidae : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ mysida : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ gobiidae_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ gobius_niger : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ palaemon_sp : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ mytilus_sp_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ scales_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ ammodytidae : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ pectinaria_sp : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ sand_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ pontoporeiidae : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ mucus : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ pontoporeia_femotara : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ remains_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ mucus_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ priapulus : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ carbon_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ wood_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ halicryptus_spinulosus_2: num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ pisces_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ mya_arenaria : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ gastrosacus : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ nephtys_ciliata : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ litter_plastics : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ clupeidae_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ pontoporeidae : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ decapoda : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ praunus_flexuosus : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ neogobius_melanostomus : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ plastics : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ agonus_cataphractus : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ clupea_harengus_2 : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ copepoda : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ halicryptus_spinolusus : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ prapulida : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ mytilus_edulis : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ hydrobia_sp : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ myoxocephalus_scorpius : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ idotea_balthica : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
#> $ caridea : num [1:5889] 0 0 0 0 0 0 0 0 0 0 ...
d_wide %>%
mutate(sum = rowSums(across(where(is.numeric)))) %>%
filter(sum == 0) %>%
distinct(na)
#> mutate: new variable 'sum' (double) with 2,166 unique values and 0% NA
#> filter: removed 4,364 rows (74%), 1,525 rows remaining
#> distinct: removed 1,524 rows (>99%), one row remaining
#> # A tibble: 1 × 1
#> na
#> <dbl>
#> 1 0
d_wide <- d_wide %>% dplyr::select(-na)
# Now make some calculations and aggregate to some taxonomic level. Since all columns are assigned to some higher level group (or the same group), the sum of these is the total stomach content. Note that I have one group for unidentified clupeids, but also sprat and herring. So if I want the total of some aggregated group, then I need to add all the sub-groups.
sort(colnames(d_wide))
#> [1] "aglae" "agonus_cataphractus"
#> [3] "algae" "ammodytidae"
#> [5] "amphipoda" "bivalvia"
#> [7] "bylgides_sarsi" "carbon"
#> [9] "carbon_2" "caridea"
#> [11] "clupea_harengus" "clupea_harengus_2"
#> [13] "clupeidae" "clupeidae_2"
#> [15] "copepoda" "crangon"
#> [17] "crangon_crangon" "crustacea"
#> [19] "cumacea" "decapoda"
#> [21] "diastylis_rathkei" "digestive_tract"
#> [23] "enchelyopus_cimbrius" "gadus_morhua"
#> [25] "gammarus_sp" "gasterosteidae"
#> [27] "gasterosteus_aculeatus" "gastrosacus"
#> [29] "gobiidae" "gobiidae_2"
#> [31] "gobius_niger" "halicryptus"
#> [33] "halicryptus_spinolusus" "halicryptus_spinulosus"
#> [35] "halicryptus_spinulosus_2" "hydrobia_sp"
#> [37] "idotea_balthica" "idotea_sp"
#> [39] "limecola_balthica" "limecola_balthica_2"
#> [41] "litter_plastics" "macoma_balthica"
#> [43] "monoporeia_affinis" "mucus"
#> [45] "mucus_2" "mya_arenaria"
#> [47] "myoxocephalus_scorpius" "mysida"
#> [49] "mysidae" "mysis_mixta"
#> [51] "mytilus_edulis" "mytilus_sp"
#> [53] "mytilus_sp_2" "neogobius_melanostomus"
#> [55] "neomysis_integer" "nephtys_ciliata"
#> [57] "palaemon_sp" "pectinaria_sp"
#> [59] "phyllodocida" "pisces"
#> [61] "pisces_2" "plastic"
#> [63] "plastics" "platichthys_flesus"
#> [65] "pleuronectidae" "polychaeta"
#> [67] "pontoporeia_femorata" "pontoporeia_femotara"
#> [69] "pontoporeidae" "pontoporeiidae"
#> [71] "prapulida" "praunus_flexuosus"
#> [73] "pred_id" "priapulida"
#> [75] "priapulida_2" "priapulus"
#> [77] "priapulus_caudatus" "pungitius_pungitius"
#> [79] "remains" "remains_2"
#> [81] "saduria_entomon" "sand"
#> [83] "sand_2" "scales"
#> [85] "scales_2" "scoloplos_armiger"
#> [87] "sprattus_sprattus" "sprattus_sprattus_2"
#> [89] "stone" "stone_2"
#> [91] "waste" "wood"
#> [93] "wood_2" "zoarces_viviparus"
d_wide2 <- d_wide %>%
mutate(amphipoda_tot = gammarus_sp + monoporeia_affinis +
amphipoda,
bivalvia_tot = bivalvia + mytilus_sp + mytilus_sp_2 + mya_arenaria + macoma_balthica +
mytilus_edulis + limecola_balthica + limecola_balthica_2,
clupeidae_tot = clupeidae + clupeidae_2,
clupea_harengus_tot = clupea_harengus + clupea_harengus_2,
gadus_morhua_tot = gadus_morhua,
gobiidae_tot = gobiidae + gobiidae_2 + gobius_niger + neogobius_melanostomus,
mysidae_tot = mysidae + neomysis_integer + mysis_mixta + mysida + gastrosacus,
non_bio_tot = stone + stone_2 + plastic + plastics + sand + wood + carbon + stone_2 + carbon_2 + wood_2 +
litter_plastics + sand_2,
other_crustacea_tot = pontoporeia_femorata + pontoporeia_femotara + crangon +
crangon_crangon + idotea_balthica + cumacea + idotea_sp +
praunus_flexuosus + crustacea + diastylis_rathkei + palaemon_sp + caridea +
copepoda + pontoporeiidae + decapoda +
pontoporeidae,
other_tot = halicryptus_spinulosus + halicryptus_spinulosus_2 + priapulus_caudatus + algae + aglae +
waste + remains + remains_2 + hydrobia_sp +
priapulida + halicryptus + digestive_tract + mucus + mucus_2 + remains_2 +
halicryptus_spinolusus + priapulida_2 + prapulida + priapulus,
other_pisces_tot = pisces + pisces_2 +
enchelyopus_cimbrius +
gasterosteus_aculeatus + scales + scales_2 +
pungitius_pungitius + zoarces_viviparus +
ammodytidae +
pleuronectidae + gasterosteidae +
agonus_cataphractus + myoxocephalus_scorpius,
platichthys_flesus_tot = platichthys_flesus,
polychaeta_tot = bylgides_sarsi + scoloplos_armiger +
phyllodocida + polychaeta + pectinaria_sp + nephtys_ciliata,
saduria_entomon_tot = saduria_entomon,
sprattus_sprattus_tot = sprattus_sprattus + sprattus_sprattus_2
)
#> mutate: new variable 'amphipoda_tot' (double) with 176 unique values and 0% NA
#> new variable 'bivalvia_tot' (double) with 661 unique values and 0% NA
#> new variable 'clupeidae_tot' (double) with 201 unique values and 0% NA
#> new variable 'clupea_harengus_tot' (double) with 287 unique values and 0% NA
#> new variable 'gadus_morhua_tot' (double) with 33 unique values and 0% NA
#> new variable 'gobiidae_tot' (double) with 151 unique values and 0% NA
#> new variable 'mysidae_tot' (double) with 298 unique values and 0% NA
#> new variable 'non_bio_tot' (double) with 62 unique values and 0% NA
#> new variable 'other_crustacea_tot' (double) with 470 unique values and 0% NA
#> new variable 'other_tot' (double) with 350 unique values and 0% NA
#> new variable 'other_pisces_tot' (double) with 235 unique values and 0% NA
#> new variable 'platichthys_flesus_tot' (double) with 4 unique values and 0% NA
#> new variable 'polychaeta_tot' (double) with 105 unique values and 0% NA
#> new variable 'saduria_entomon_tot' (double) with 431 unique values and 0% NA
#> new variable 'sprattus_sprattus_tot' (double) with 518 unique values and 0% NA
# Select only columns aggregated columns (ending with _tot) (all columns (prey) are represented there)
colnames(d_wide2)
#> [1] "pred_id" "diastylis_rathkei"
#> [3] "halicryptus_spinulosus" "bylgides_sarsi"
#> [5] "clupeidae" "sprattus_sprattus"
#> [7] "mysis_mixta" "stone"
#> [9] "crangon_crangon" "gammarus_sp"
#> [11] "priapulida" "priapulus_caudatus"
#> [13] "gasterosteus_aculeatus" "pisces"
#> [15] "saduria_entomon" "neomysis_integer"
#> [17] "clupea_harengus" "monoporeia_affinis"
#> [19] "scales" "waste"
#> [21] "gobiidae" "pontoporeia_femorata"
#> [23] "remains" "crustacea"
#> [25] "mysidae" "limecola_balthica"
#> [27] "bivalvia" "halicryptus"
#> [29] "zoarces_viviparus" "platichthys_flesus"
#> [31] "digestive_tract" "wood"
#> [33] "phyllodocida" "polychaeta"
#> [35] "sand" "mytilus_sp"
#> [37] "amphipoda" "limecola_balthica_2"
#> [39] "algae" "pungitius_pungitius"
#> [41] "scoloplos_armiger" "gadus_morhua"
#> [43] "priapulida_2" "idotea_sp"
#> [45] "enchelyopus_cimbrius" "pleuronectidae"
#> [47] "cumacea" "plastic"
#> [49] "stone_2" "crangon"
#> [51] "sprattus_sprattus_2" "aglae"
#> [53] "macoma_balthica" "carbon"
#> [55] "gasterosteidae" "mysida"
#> [57] "gobiidae_2" "gobius_niger"
#> [59] "palaemon_sp" "mytilus_sp_2"
#> [61] "scales_2" "ammodytidae"
#> [63] "pectinaria_sp" "sand_2"
#> [65] "pontoporeiidae" "mucus"
#> [67] "pontoporeia_femotara" "remains_2"
#> [69] "mucus_2" "priapulus"
#> [71] "carbon_2" "wood_2"
#> [73] "halicryptus_spinulosus_2" "pisces_2"
#> [75] "mya_arenaria" "gastrosacus"
#> [77] "nephtys_ciliata" "litter_plastics"
#> [79] "clupeidae_2" "pontoporeidae"
#> [81] "decapoda" "praunus_flexuosus"
#> [83] "neogobius_melanostomus" "plastics"
#> [85] "agonus_cataphractus" "clupea_harengus_2"
#> [87] "copepoda" "halicryptus_spinolusus"
#> [89] "prapulida" "mytilus_edulis"
#> [91] "hydrobia_sp" "myoxocephalus_scorpius"
#> [93] "idotea_balthica" "caridea"
#> [95] "amphipoda_tot" "bivalvia_tot"
#> [97] "clupeidae_tot" "clupea_harengus_tot"
#> [99] "gadus_morhua_tot" "gobiidae_tot"
#> [101] "mysidae_tot" "non_bio_tot"
#> [103] "other_crustacea_tot" "other_tot"
#> [105] "other_pisces_tot" "platichthys_flesus_tot"
#> [107] "polychaeta_tot" "saduria_entomon_tot"
#> [109] "sprattus_sprattus_tot"
d_wide3 <- d_wide2 %>%
dplyr::select(pred_id, ends_with("_tot"))
# Add back in other information about the predator ID
d_sel <- d %>%
dplyr::select(predator_latin_name, species, pred_weight_g, pred_length_cm,
year, quarter, month, day, ices_rect, subdiv, haul_id,
X, Y, lat, lon, pred_id, depth, pred_weight_source, cruise,
fle_kg_km2, lcod_kg_km2, scod_kg_km2
) %>%
distinct(pred_id, .keep_all = TRUE)
#> distinct: removed 51,802 rows (90%), 5,928 rows remaining
d_wide3 <- left_join(d_wide3, d_sel) %>% filter(year > 1992)
#> Joining, by = "pred_id"
#> left_join: added 21 columns (predator_latin_name, species, pred_weight_g, pred_length_cm, year, …)
#> > rows only in x 0
#> > rows only in y ( 39)
#> > matched rows 5,889
#> > =======
#> > rows total 5,889
#> filter: no rows removed
# Make separate data frames for cod and flounder. They are wide so that we can easily add columns when calculating aggregate response variables (sums across prey groups)
d_wide_cod <- d_wide3 %>% filter(grepl("COD", pred_id))
#> filter: removed 2,579 rows (44%), 3,310 rows remaining
d_wide_fle <- d_wide3 %>% filter(grepl("FLE", pred_id))
#> filter: removed 3,310 rows (56%), 2,579 rows remainingFind which prey are shared for cod and flounder
long_cod <- d_wide_cod %>%
pivot_longer(cols = ends_with("_tot"), names_to = "prey_group", values_to = "tot_prey_weight")
#> pivot_longer: reorganized (amphipoda_tot, bivalvia_tot, clupeidae_tot, clupea_harengus_tot, gadus_morhua_tot, …) into (prey_group, tot_prey_weight) [was 3310x37, now 49650x24]
long_fle <- d_wide_fle %>%
pivot_longer(cols = ends_with("_tot"), names_to = "prey_group", values_to = "tot_prey_weight")
#> pivot_longer: reorganized (amphipoda_tot, bivalvia_tot, clupeidae_tot, clupea_harengus_tot, gadus_morhua_tot, …) into (prey_group, tot_prey_weight) [was 2579x37, now 38685x24]s_cod_important_prey <- long_cod %>%
filter(pred_length_cm <= 25) %>%
group_by(prey_group, year, quarter) %>%
summarise(prey_group_tot = sum(tot_prey_weight)) %>%
ungroup() %>%
group_by(year, quarter) %>%
mutate(percent_by_group = 100 * (prey_group_tot / sum(prey_group_tot))) %>%
ungroup()
#> filter: removed 32,310 rows (65%), 17,340 rows remaining
#> group_by: 3 grouping variables (prey_group, year, quarter)
#> summarise: now 120 rows and 4 columns, 2 group variables remaining (prey_group, year)
#> ungroup: no grouping variables
#> group_by: 2 grouping variables (year, quarter)
#> mutate (grouped): new variable 'percent_by_group' (double) with 82 unique values and 0% NA
#> ungroup: no grouping variables
l_cod_important_prey <- long_cod %>%
filter(pred_length_cm > 25) %>%
group_by(prey_group, year, quarter) %>%
summarise(prey_group_tot = sum(tot_prey_weight)) %>%
ungroup() %>%
group_by(year, quarter) %>%
mutate(percent_by_group = 100 * (prey_group_tot / sum(prey_group_tot))) %>%
ungroup()
#> filter: removed 17,340 rows (35%), 32,310 rows remaining
#> group_by: 3 grouping variables (prey_group, year, quarter)
#> summarise: now 120 rows and 4 columns, 2 group variables remaining (prey_group, year)
#> ungroup: no grouping variables
#> group_by: 2 grouping variables (year, quarter)
#> mutate (grouped): new variable 'percent_by_group' (double) with 113 unique values and 0% NA
#> ungroup: no grouping variables
fle_important_prey <- long_fle %>%
group_by(prey_group, year, quarter) %>%
summarise(prey_group_tot = sum(tot_prey_weight)) %>%
ungroup() %>%
group_by(year, quarter) %>%
mutate(percent_by_group = 100 * (prey_group_tot / sum(prey_group_tot))) %>%
ungroup() %>%
mutate(predator = "flounder")
#> group_by: 3 grouping variables (prey_group, year, quarter)
#> summarise: now 120 rows and 4 columns, 2 group variables remaining (prey_group, year)
#> ungroup: no grouping variables
#> group_by: 2 grouping variables (year, quarter)
#> mutate (grouped): new variable 'percent_by_group' (double) with 91 unique values and 0% NA
#> ungroup: no grouping variables
#> mutate: new variable 'predator' (character) with one unique value and 0% NA
s_cod_important_prey %>%
ggplot(aes(x = reorder(prey_group, desc(percent_by_group)), y = percent_by_group)) +
geom_bar(stat = "identity", position = position_dodge()) +
theme_classic(base_size = 16) +
facet_grid(quarter~year) +
theme(axis.text.x = element_text(angle = 90, size = 6)) +
labs(x = "Prey group", y = "Percent") +
NULL
s_cod_important_prey %>%
ggplot(aes(x = reorder(prey_group, desc(percent_by_group)), y = percent_by_group)) +
geom_bar(stat = "identity", position = position_dodge()) +
theme_classic(base_size = 16) +
facet_grid(quarter~year) +
theme(axis.text.x = element_text(angle = 90, size = 6)) +
labs(x = "Prey group", y = "Percent") +
NULL
fle_important_prey %>%
ggplot(aes(x = reorder(prey_group, desc(percent_by_group)), y = percent_by_group)) +
geom_bar(stat = "identity", position = position_dodge()) +
theme_classic(base_size = 16) +
facet_grid(quarter~year) +
theme(axis.text.x = element_text(angle = 90, size = 6)) +
labs(x = "Prey group", y = "Percent") +
NULL
# Aggregate all data
s_cod_important_prey2 <- long_cod %>%
filter(pred_length_cm <= 25) %>%
group_by(prey_group, quarter) %>%
summarise(sum_tot_prey_weight = sum(tot_prey_weight)) %>%
ungroup() %>%
group_by(quarter) %>%
mutate(percent_by_group = 100 * (sum_tot_prey_weight / sum(sum_tot_prey_weight))) %>%
ungroup() %>%
mutate(predator = "Cod <= 25 cm")
#> filter: removed 32,310 rows (65%), 17,340 rows remaining
#> group_by: 2 grouping variables (prey_group, quarter)
#> summarise: now 30 rows and 3 columns, one group variable remaining (prey_group)
#> ungroup: no grouping variables
#> group_by: one grouping variable (quarter)
#> mutate (grouped): new variable 'percent_by_group' (double) with 26 unique values and 0% NA
#> ungroup: no grouping variables
#> mutate: new variable 'predator' (character) with one unique value and 0% NA
l_cod_important_prey2 <- long_cod %>%
filter(pred_length_cm > 25) %>%
group_by(prey_group, quarter) %>%
summarise(sum_tot_prey_weight = sum(tot_prey_weight)) %>%
ungroup() %>%
group_by(quarter) %>%
mutate(percent_by_group = 100 * (sum_tot_prey_weight / sum(sum_tot_prey_weight))) %>%
ungroup() %>%
mutate(predator = "Cod > 25 cm")
#> filter: removed 17,340 rows (35%), 32,310 rows remaining
#> group_by: 2 grouping variables (prey_group, quarter)
#> summarise: now 30 rows and 3 columns, one group variable remaining (prey_group)
#> ungroup: no grouping variables
#> group_by: one grouping variable (quarter)
#> mutate (grouped): new variable 'percent_by_group' (double) with 30 unique values and 0% NA
#> ungroup: no grouping variables
#> mutate: new variable 'predator' (character) with one unique value and 0% NA
fle_important_prey2 <- long_fle %>%
group_by(prey_group, quarter) %>%
summarise(sum_tot_prey_weight = sum(tot_prey_weight)) %>%
ungroup() %>%
group_by(quarter) %>%
mutate(percent_by_group = 100 * (sum_tot_prey_weight / sum(sum_tot_prey_weight))) %>%
ungroup() %>%
mutate(predator = "Flounder")
#> group_by: 2 grouping variables (prey_group, quarter)
#> summarise: now 30 rows and 3 columns, one group variable remaining (prey_group)
#> ungroup: no grouping variables
#> group_by: one grouping variable (quarter)
#> mutate (grouped): new variable 'percent_by_group' (double) with 27 unique values and 0% NA
#> ungroup: no grouping variables
#> mutate: new variable 'predator' (character) with one unique value and 0% NA
plotdat <- bind_rows(s_cod_important_prey2, l_cod_important_prey2, fle_important_prey2)
plotdat %>%
ggplot(aes(x = reorder(prey_group, desc(percent_by_group)), y = percent_by_group)) +
geom_bar(stat = "identity", position = position_dodge()) +
theme_classic(base_size = 16) +
theme(axis.text.x = element_text(angle = 90)) +
labs(x = "Prey group", y = "Percent") +
facet_grid(quarter~predator) +
theme(legend.text = element_text(size = 4)) +
NULL
plotdat %>% arrange(desc(percent_by_group)) %>% distinct(prey_group) %>% as.data.frame()
#> distinct: removed 75 rows (83%), 15 rows remaining
#> prey_group
#> 1 bivalvia_tot
#> 2 mysidae_tot
#> 3 clupea_harengus_tot
#> 4 sprattus_sprattus_tot
#> 5 saduria_entomon_tot
#> 6 other_crustacea_tot
#> 7 gobiidae_tot
#> 8 other_tot
#> 9 other_pisces_tot
#> 10 gadus_morhua_tot
#> 11 polychaeta_tot
#> 12 clupeidae_tot
#> 13 platichthys_flesus_tot
#> 14 amphipoda_tot
#> 15 non_bio_tot
plotdat %>%
mutate(prey_group = ifelse(prey_group == "bivalvia_tot", "Bivalvia", prey_group),
prey_group = ifelse(prey_group == "mysidae_tot", "Mysidae", prey_group),
prey_group = ifelse(prey_group == "clupea_harengus_tot", "Clupea harengus", prey_group),
prey_group = ifelse(prey_group == "sprattus_sprattus_tot", "Sprattus sprattus", prey_group),
prey_group = ifelse(prey_group == "saduria_entomon_tot", "Saduria entomon", prey_group),
prey_group = ifelse(prey_group == "other_crustacea_tot", "Other crustacea", prey_group),
prey_group = ifelse(prey_group == "gobiidae_tot", "Gobiidae", prey_group),
prey_group = ifelse(prey_group == "other_tot", "Other", prey_group),
prey_group = ifelse(prey_group == "other_pisces_tot", "Other pisces", prey_group),
prey_group = ifelse(prey_group == "gadus_morhua_tot", "Gadus morhua", prey_group),
prey_group = ifelse(prey_group == "polychaeta_tot", "Polychaeta", prey_group),
prey_group = ifelse(prey_group == "clupeidae_tot", "Clupeidae", prey_group),
prey_group = ifelse(prey_group == "platichthys_flesus_tot", "platichthys_flesus", prey_group),
prey_group = ifelse(prey_group == "amphipoda_tot", "Amphipoda", prey_group),
prey_group = ifelse(prey_group == "non_bio_tot", "Non-bio", prey_group)) %>%
ggplot(aes(x = reorder(prey_group, desc(percent_by_group)), y = percent_by_group,
fill = predator)) +
geom_bar(stat = "identity", position = position_dodge()) +
theme(axis.text.x = element_text(angle = 90),
legend.position = c(0.8, 0.8)) +
labs(x = "Prey group", y = "Percent", fill = "Predator") +
NULL
#> mutate: changed 90 values (100%) of 'prey_group' (0 new NA)
ggsave("figures/supp/bar_diet_comp.pdf", width = 17, height = 17, units = "cm")colourCount <- length(unique(plotdat$prey_group))
getPalette <- colorRampPalette(brewer.pal(12, "Paired"))
pal <- getPalette(colourCount)
plotdat$pred_new <- factor(plotdat$predator, levels = c("Flounder", "Cod <= 25 cm", "Cod > 25 cm"))
plotdat %>%
mutate(prey_group = ifelse(prey_group == "bivalvia_tot", "Bivalvia", prey_group),
prey_group = ifelse(prey_group == "mysidae_tot", "Mysidae", prey_group),
prey_group = ifelse(prey_group == "gadus_morhua_tot", "Gadus morhua", prey_group),
prey_group = ifelse(prey_group == "saduria_entomon_tot", "Saduria entomon", prey_group),
prey_group = ifelse(prey_group == "gobiidae_tot", "Gobiidae", prey_group),
prey_group = ifelse(prey_group == "other_crustacea_tot", "Other crustacea", prey_group),
prey_group = ifelse(prey_group == "polychaeta_tot", "Polychaeta", prey_group),
prey_group = ifelse(prey_group == "platichthys_flesus_tot", "Platichthys flesus", prey_group),
prey_group = ifelse(prey_group == "sprattus_sprattus_tot", "Sprattus sprattus", prey_group),
prey_group = ifelse(prey_group == "other_pisces_tot", "Other pisces", prey_group),
prey_group = ifelse(prey_group == "clupeidae_tot", "Clupeidae", prey_group),
prey_group = ifelse(prey_group == "clupea_harengus_tot", "Clupea harengus", prey_group),
prey_group = ifelse(prey_group == "other_tot", "Other", prey_group),
prey_group = ifelse(prey_group == "amphipoda_tot", "Amphipoda", prey_group),
prey_group = ifelse(prey_group == "non_bio_tot", "Non-bio", prey_group)) %>%
ggplot(aes(x = "" , y = percent_by_group, fill = fct_inorder(prey_group))) +
facet_wrap(~pred_new) +
labs(x = "", y = "") +
geom_col(width = 1, color = NA, alpha = 0.8) +
coord_polar(theta = "y") +
scale_fill_manual(values = pal) +
guides(fill = guide_legend(title.position = "top", title = "Prey group")) +
theme_plot() +
theme(legend.position = "bottom",
axis.text = element_blank(),
legend.text = element_text(size = 8),
axis.ticks = element_blank(),
panel.grid = element_blank())
#> mutate: changed 90 values (100%) of 'prey_group' (0 new NA)
ggsave("figures/supp/total_pie.pdf", width = 17, height = 17, units = "cm")# Prop from prey by size class, years and quarters pooled
long_cod %>% filter(pred_length_cm < 6) %>% arrange(desc(pred_length_cm)) %>% as.data.frame()
#> filter: removed 49,320 rows (99%), 330 rows remaining
#> pred_id predator_latin_name species pred_weight_g pred_length_cm
#> 1 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 2 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 3 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 4 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 5 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 6 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 7 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 8 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 9 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 10 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 11 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 12 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 13 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 14 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 15 2015_4_COD_11 Gadus morhua Cod 1.00 5
#> 16 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 17 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 18 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 19 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 20 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 21 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 22 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 23 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 24 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 25 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 26 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 27 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 28 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 29 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 30 2015_4_COD_410 Gadus morhua Cod 0.90 5
#> 31 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 32 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 33 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 34 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 35 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 36 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 37 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 38 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 39 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 40 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 41 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 42 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 43 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 44 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 45 2016_1_COD_656 Gadus morhua Cod 1.25 5
#> 46 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 47 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 48 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 49 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 50 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 51 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 52 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 53 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 54 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 55 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 56 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 57 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 58 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 59 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 60 2016_1_COD_657 Gadus morhua Cod 1.25 5
#> 61 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 62 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 63 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 64 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 65 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 66 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 67 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 68 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 69 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 70 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 71 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 72 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 73 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 74 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 75 2017_1_COD_505 Gadus morhua Cod 1.00 5
#> 76 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 77 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 78 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 79 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 80 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 81 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 82 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 83 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 84 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 85 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 86 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 87 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 88 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 89 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 90 2017_1_COD_586 Gadus morhua Cod 1.00 5
#> 91 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 92 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 93 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 94 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 95 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 96 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 97 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 98 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 99 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 100 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 101 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 102 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 103 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 104 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 105 2017_4_COD_101 Gadus morhua Cod 1.00 5
#> 106 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 107 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 108 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 109 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 110 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 111 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 112 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 113 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 114 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 115 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 116 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 117 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 118 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 119 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 120 2017_4_COD_129 Gadus morhua Cod 1.00 5
#> 121 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 122 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 123 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 124 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 125 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 126 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 127 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 128 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 129 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 130 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 131 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 132 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 133 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 134 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 135 2017_4_COD_71 Gadus morhua Cod 1.00 5
#> 136 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 137 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 138 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 139 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 140 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 141 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 142 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 143 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 144 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 145 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 146 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 147 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 148 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 149 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 150 2018_1_COD_606 Gadus morhua Cod 1.36 5
#> 151 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 152 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 153 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 154 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 155 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 156 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 157 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 158 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 159 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 160 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 161 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 162 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 163 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 164 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 165 2019_4_COD_109 Gadus morhua Cod 1.33 5
#> 166 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 167 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 168 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 169 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 170 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 171 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 172 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 173 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 174 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 175 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 176 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 177 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 178 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 179 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 180 2019_4_COD_144 Gadus morhua Cod 1.29 5
#> 181 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 182 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 183 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 184 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 185 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 186 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 187 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 188 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 189 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 190 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 191 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 192 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 193 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 194 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 195 2019_4_COD_176 Gadus morhua Cod 1.22 5
#> 196 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 197 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 198 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 199 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 200 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 201 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 202 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 203 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 204 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 205 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 206 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 207 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 208 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 209 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 210 2019_4_COD_202 Gadus morhua Cod 1.48 5
#> 211 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 212 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 213 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 214 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 215 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 216 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 217 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 218 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 219 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 220 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 221 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 222 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 223 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 224 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 225 2019_4_COD_278 Gadus morhua Cod 1.17 5
#> 226 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 227 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 228 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 229 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 230 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 231 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 232 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 233 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 234 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 235 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 236 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 237 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 238 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 239 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 240 2019_4_COD_300 Gadus morhua Cod 0.98 5
#> 241 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 242 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 243 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 244 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 245 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 246 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 247 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 248 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 249 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 250 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 251 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 252 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 253 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 254 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 255 2019_4_COD_326 Gadus morhua Cod 0.84 5
#> 256 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 257 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 258 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 259 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 260 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 261 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 262 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 263 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 264 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 265 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 266 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 267 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 268 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 269 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 270 2019_4_COD_346 Gadus morhua Cod 1.25 5
#> 271 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 272 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 273 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 274 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 275 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 276 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 277 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 278 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 279 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 280 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 281 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 282 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 283 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 284 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 285 2019_4_COD_53 Gadus morhua Cod 0.88 5
#> 286 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 287 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 288 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 289 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 290 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 291 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 292 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 293 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 294 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 295 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 296 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 297 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 298 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 299 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 300 2019_4_COD_79 Gadus morhua Cod 1.38 5
#> 301 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 302 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 303 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 304 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 305 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 306 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 307 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 308 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 309 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 310 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 311 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 312 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 313 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 314 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 315 2017_4_COD_15 Gadus morhua Cod 1.00 4
#> 316 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 317 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 318 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 319 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 320 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 321 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 322 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 323 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 324 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 325 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 326 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 327 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 328 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 329 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> 330 2017_4_COD_57 Gadus morhua Cod 1.00 4
#> year quarter month day ices_rect subdiv haul_id X Y
#> 1 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 2 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 3 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 4 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 5 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 6 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 7 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 8 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 9 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 10 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 11 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 12 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 13 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 14 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 15 2015 4 11 20 40G4 25 2015_4_2 474.8173 6165.344
#> 16 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 17 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 18 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 19 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 20 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 21 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 22 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 23 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 24 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 25 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 26 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 27 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 28 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 29 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 30 2015 4 11 23 43G8 28 2015_4_21 728.9385 6338.581
#> 31 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 32 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 33 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 34 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 35 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 36 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 37 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 38 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 39 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 40 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 41 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 42 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 43 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 44 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 45 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 46 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 47 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 48 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 49 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 50 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 51 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 52 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 53 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 54 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 55 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 56 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 57 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 58 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 59 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 60 2016 1 2 NA 44G9 28 2016_1_55 761.9226 6422.421
#> 61 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 62 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 63 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 64 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 65 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 66 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 67 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 68 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 69 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 70 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 71 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 72 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 73 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 74 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 75 2017 1 3 3 41G8 26 2017_1_73 713.0776 6252.234
#> 76 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 77 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 78 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 79 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 80 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 81 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 82 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 83 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 84 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 85 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 86 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 87 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 88 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 89 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 90 2017 1 3 4 40G8 26 2017_1_82 729.1156 6202.880
#> 91 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 92 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 93 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 94 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 95 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 96 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 97 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 98 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 99 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 100 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 101 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 102 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 103 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 104 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 105 2017 4 11 NA 40G4 25 2017_4_7 489.5205 6170.849
#> 106 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 107 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 108 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 109 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 110 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 111 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 112 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 113 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 114 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 115 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 116 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 117 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 118 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 119 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 120 2017 4 11 NA 40G5 25 2017_4_10 515.6724 6183.849
#> 121 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 122 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 123 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 124 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 125 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 126 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 127 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 128 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 129 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 130 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 131 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 132 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 133 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 134 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 135 2017 4 11 NA 40G4 25 2017_4_6 474.7959 6161.635
#> 136 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 137 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 138 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 139 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 140 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 141 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 142 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 143 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 144 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 145 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 146 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 147 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 148 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 149 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 150 2018 1 3 NA 40G4 25 2018_1_62 459.1304 6171.028
#> 151 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 152 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 153 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 154 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 155 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 156 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 157 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 158 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 159 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 160 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 161 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 162 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 163 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 164 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 165 2019 4 11 NA 40G4 25 2019_4_68 460.1953 6172.873
#> 166 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 167 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 168 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 169 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 170 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 171 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 172 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 173 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 174 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 175 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 176 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 177 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 178 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 179 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 180 2019 4 11 NA 40G4 25 2019_4_69 465.4179 6170.973
#> 181 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 182 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 183 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 184 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 185 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 186 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 187 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 188 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 189 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 190 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 191 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 192 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 193 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 194 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 195 2019 4 11 NA 40G4 25 2019_4_70 468.5617 6170.950
#> 196 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 197 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 198 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 199 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 200 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 201 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 202 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 203 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 204 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 205 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 206 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 207 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 208 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 209 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 210 2019 4 11 NA 40G4 25 2019_4_71 460.1953 6172.873
#> 211 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 212 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 213 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 214 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 215 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 216 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 217 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 218 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 219 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 220 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 221 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 222 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 223 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 224 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 225 2019 4 11 NA 41G8 26 2019_4_86 715.0414 6254.191
#> 226 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 227 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 228 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 229 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 230 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 231 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 232 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 233 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 234 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 235 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 236 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 237 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 238 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 239 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 240 2019 4 11 27 41G8 26 2019_4_93 712.2339 6248.475
#> 241 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 242 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 243 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 244 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 245 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 246 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 247 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 248 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 249 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 250 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 251 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 252 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 253 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 254 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 255 2019 4 11 NA 40G5 25 2019_4_95 535.4783 6189.530
#> 256 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 257 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 258 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 259 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 260 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 261 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 262 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 263 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 264 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 265 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 266 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 267 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 268 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 269 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 270 2019 4 11 NA 40G4 25 2019_4_97 465.4179 6170.973
#> 271 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 272 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 273 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 274 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 275 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 276 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 277 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 278 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 279 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 280 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 281 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 282 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 283 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 284 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 285 2019 4 11 NA 39G4 24 2019_4_66 469.4290 6144.975
#> 286 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 287 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 288 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 289 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 290 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 291 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 292 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 293 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 294 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 295 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 296 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 297 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 298 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 299 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 300 2019 4 11 NA 40G4 25 2019_4_67 460.1783 6171.018
#> 301 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 302 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 303 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 304 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 305 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 306 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 307 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 308 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 309 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 310 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 311 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 312 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 313 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 314 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 315 2017 4 11 NA 39G4 24 2017_4_2 469.4290 6144.975
#> 316 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 317 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 318 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 319 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 320 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 321 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 322 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 323 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 324 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 325 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 326 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 327 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 328 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 329 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> 330 2017 4 11 NA 39G4 24 2017_4_4 477.8717 6146.779
#> lat lon depth pred_weight_source cruise fle_kg_km2
#> 1 55.63333 14.60000 60.2 measured BITS 330.2812
#> 2 55.63333 14.60000 60.2 measured BITS 330.2812
#> 3 55.63333 14.60000 60.2 measured BITS 330.2812
#> 4 55.63333 14.60000 60.2 measured BITS 330.2812
#> 5 55.63333 14.60000 60.2 measured BITS 330.2812
#> 6 55.63333 14.60000 60.2 measured BITS 330.2812
#> 7 55.63333 14.60000 60.2 measured BITS 330.2812
#> 8 55.63333 14.60000 60.2 measured BITS 330.2812
#> 9 55.63333 14.60000 60.2 measured BITS 330.2812
#> 10 55.63333 14.60000 60.2 measured BITS 330.2812
#> 11 55.63333 14.60000 60.2 measured BITS 330.2812
#> 12 55.63333 14.60000 60.2 measured BITS 330.2812
#> 13 55.63333 14.60000 60.2 measured BITS 330.2812
#> 14 55.63333 14.60000 60.2 measured BITS 330.2812
#> 15 55.63333 14.60000 60.2 measured BITS 330.2812
#> 16 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 17 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 18 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 19 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 20 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 21 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 22 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 23 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 24 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 25 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 26 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 27 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 28 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 29 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 30 57.13333 18.78333 40.8 measured BITS 5018.8073
#> 31 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 32 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 33 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 34 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 35 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 36 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 37 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 38 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 39 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 40 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 41 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 42 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 43 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 44 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 45 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 46 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 47 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 48 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 49 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 50 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 51 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 52 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 53 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 54 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 55 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 56 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 57 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 58 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 59 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 60 57.86667 19.41667 49.0 estimated_from_length BITS 1962.0690
#> 61 56.36667 18.45000 38.0 measured BITS 275.3690
#> 62 56.36667 18.45000 38.0 measured BITS 275.3690
#> 63 56.36667 18.45000 38.0 measured BITS 275.3690
#> 64 56.36667 18.45000 38.0 measured BITS 275.3690
#> 65 56.36667 18.45000 38.0 measured BITS 275.3690
#> 66 56.36667 18.45000 38.0 measured BITS 275.3690
#> 67 56.36667 18.45000 38.0 measured BITS 275.3690
#> 68 56.36667 18.45000 38.0 measured BITS 275.3690
#> 69 56.36667 18.45000 38.0 measured BITS 275.3690
#> 70 56.36667 18.45000 38.0 measured BITS 275.3690
#> 71 56.36667 18.45000 38.0 measured BITS 275.3690
#> 72 56.36667 18.45000 38.0 measured BITS 275.3690
#> 73 56.36667 18.45000 38.0 measured BITS 275.3690
#> 74 56.36667 18.45000 38.0 measured BITS 275.3690
#> 75 56.36667 18.45000 38.0 measured BITS 275.3690
#> 76 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 77 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 78 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 79 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 80 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 81 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 82 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 83 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 84 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 85 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 86 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 87 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 88 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 89 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 90 55.91667 18.66667 113.0 measured BITS 9830.2637
#> 91 55.68333 14.83333 57.0 measured BITS 872.0721
#> 92 55.68333 14.83333 57.0 measured BITS 872.0721
#> 93 55.68333 14.83333 57.0 measured BITS 872.0721
#> 94 55.68333 14.83333 57.0 measured BITS 872.0721
#> 95 55.68333 14.83333 57.0 measured BITS 872.0721
#> 96 55.68333 14.83333 57.0 measured BITS 872.0721
#> 97 55.68333 14.83333 57.0 measured BITS 872.0721
#> 98 55.68333 14.83333 57.0 measured BITS 872.0721
#> 99 55.68333 14.83333 57.0 measured BITS 872.0721
#> 100 55.68333 14.83333 57.0 measured BITS 872.0721
#> 101 55.68333 14.83333 57.0 measured BITS 872.0721
#> 102 55.68333 14.83333 57.0 measured BITS 872.0721
#> 103 55.68333 14.83333 57.0 measured BITS 872.0721
#> 104 55.68333 14.83333 57.0 measured BITS 872.0721
#> 105 55.68333 14.83333 57.0 measured BITS 872.0721
#> 106 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 107 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 108 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 109 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 110 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 111 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 112 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 113 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 114 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 115 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 116 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 117 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 118 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 119 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 120 55.80000 15.25000 52.0 measured BITS 1351.4823
#> 121 55.60000 14.60000 65.0 measured BITS 403.2579
#> 122 55.60000 14.60000 65.0 measured BITS 403.2579
#> 123 55.60000 14.60000 65.0 measured BITS 403.2579
#> 124 55.60000 14.60000 65.0 measured BITS 403.2579
#> 125 55.60000 14.60000 65.0 measured BITS 403.2579
#> 126 55.60000 14.60000 65.0 measured BITS 403.2579
#> 127 55.60000 14.60000 65.0 measured BITS 403.2579
#> 128 55.60000 14.60000 65.0 measured BITS 403.2579
#> 129 55.60000 14.60000 65.0 measured BITS 403.2579
#> 130 55.60000 14.60000 65.0 measured BITS 403.2579
#> 131 55.60000 14.60000 65.0 measured BITS 403.2579
#> 132 55.60000 14.60000 65.0 measured BITS 403.2579
#> 133 55.60000 14.60000 65.0 measured BITS 403.2579
#> 134 55.60000 14.60000 65.0 measured BITS 403.2579
#> 135 55.60000 14.60000 65.0 measured BITS 403.2579
#> 136 55.68333 14.35000 34.0 measured BITS 405.5710
#> 137 55.68333 14.35000 34.0 measured BITS 405.5710
#> 138 55.68333 14.35000 34.0 measured BITS 405.5710
#> 139 55.68333 14.35000 34.0 measured BITS 405.5710
#> 140 55.68333 14.35000 34.0 measured BITS 405.5710
#> 141 55.68333 14.35000 34.0 measured BITS 405.5710
#> 142 55.68333 14.35000 34.0 measured BITS 405.5710
#> 143 55.68333 14.35000 34.0 measured BITS 405.5710
#> 144 55.68333 14.35000 34.0 measured BITS 405.5710
#> 145 55.68333 14.35000 34.0 measured BITS 405.5710
#> 146 55.68333 14.35000 34.0 measured BITS 405.5710
#> 147 55.68333 14.35000 34.0 measured BITS 405.5710
#> 148 55.68333 14.35000 34.0 measured BITS 405.5710
#> 149 55.68333 14.35000 34.0 measured BITS 405.5710
#> 150 55.68333 14.35000 34.0 measured BITS 405.5710
#> 151 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 152 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 153 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 154 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 155 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 156 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 157 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 158 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 159 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 160 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 161 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 162 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 163 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 164 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 165 55.70000 14.36667 37.0 measured BITS 2428.5714
#> 166 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 167 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 168 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 169 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 170 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 171 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 172 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 173 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 174 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 175 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 176 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 177 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 178 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 179 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 180 55.68333 14.45000 49.0 measured BITS 2550.6742
#> 181 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 182 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 183 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 184 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 185 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 186 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 187 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 188 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 189 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 190 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 191 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 192 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 193 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 194 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 195 55.68333 14.50000 46.0 measured BITS 1147.2000
#> 196 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 197 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 198 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 199 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 200 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 201 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 202 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 203 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 204 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 205 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 206 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 207 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 208 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 209 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 210 55.70000 14.36667 37.0 measured BITS 1373.9846
#> 211 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 212 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 213 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 214 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 215 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 216 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 217 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 218 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 219 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 220 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 221 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 222 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 223 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 224 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 225 56.38333 18.48333 36.0 measured BITS 1131.2849
#> 226 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 227 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 228 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 229 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 230 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 231 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 232 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 233 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 234 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 235 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 236 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 237 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 238 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 239 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 240 56.33333 18.43333 41.0 measured BITS 1124.8833
#> 241 55.85000 15.56667 34.0 measured BITS 658.5475
#> 242 55.85000 15.56667 34.0 measured BITS 658.5475
#> 243 55.85000 15.56667 34.0 measured BITS 658.5475
#> 244 55.85000 15.56667 34.0 measured BITS 658.5475
#> 245 55.85000 15.56667 34.0 measured BITS 658.5475
#> 246 55.85000 15.56667 34.0 measured BITS 658.5475
#> 247 55.85000 15.56667 34.0 measured BITS 658.5475
#> 248 55.85000 15.56667 34.0 measured BITS 658.5475
#> 249 55.85000 15.56667 34.0 measured BITS 658.5475
#> 250 55.85000 15.56667 34.0 measured BITS 658.5475
#> 251 55.85000 15.56667 34.0 measured BITS 658.5475
#> 252 55.85000 15.56667 34.0 measured BITS 658.5475
#> 253 55.85000 15.56667 34.0 measured BITS 658.5475
#> 254 55.85000 15.56667 34.0 measured BITS 658.5475
#> 255 55.85000 15.56667 34.0 measured BITS 658.5475
#> 256 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 257 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 258 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 259 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 260 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 261 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 262 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 263 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 264 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 265 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 266 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 267 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 268 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 269 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 270 55.68333 14.45000 44.0 measured BITS 1978.4467
#> 271 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 272 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 273 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 274 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 275 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 276 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 277 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 278 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 279 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 280 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 281 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 282 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 283 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 284 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 285 55.45000 14.51667 58.0 measured BITS 22007.4419
#> 286 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 287 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 288 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 289 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 290 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 291 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 292 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 293 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 294 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 295 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 296 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 297 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 298 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 299 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 300 55.68333 14.36667 34.7 measured BITS 2146.8571
#> 301 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 302 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 303 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 304 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 305 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 306 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 307 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 308 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 309 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 310 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 311 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 312 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 313 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 314 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 315 55.45000 14.51667 57.0 measured BITS 4702.1503
#> 316 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 317 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 318 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 319 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 320 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 321 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 322 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 323 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 324 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 325 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 326 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 327 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 328 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 329 55.46667 14.65000 67.0 measured BITS 3260.8137
#> 330 55.46667 14.65000 67.0 measured BITS 3260.8137
#> lcod_kg_km2 scod_kg_km2 prey_group tot_prey_weight
#> 1 41403.147917 1.202027e+04 amphipoda_tot 0.00
#> 2 41403.147917 1.202027e+04 bivalvia_tot 0.00
#> 3 41403.147917 1.202027e+04 clupeidae_tot 0.00
#> 4 41403.147917 1.202027e+04 clupea_harengus_tot 0.00
#> 5 41403.147917 1.202027e+04 gadus_morhua_tot 0.00
#> 6 41403.147917 1.202027e+04 gobiidae_tot 0.00
#> 7 41403.147917 1.202027e+04 mysidae_tot 0.00
#> 8 41403.147917 1.202027e+04 non_bio_tot 0.00
#> 9 41403.147917 1.202027e+04 other_crustacea_tot 0.00
#> 10 41403.147917 1.202027e+04 other_tot 0.00
#> 11 41403.147917 1.202027e+04 other_pisces_tot 0.00
#> 12 41403.147917 1.202027e+04 platichthys_flesus_tot 0.00
#> 13 41403.147917 1.202027e+04 polychaeta_tot 0.00
#> 14 41403.147917 1.202027e+04 saduria_entomon_tot 0.00
#> 15 41403.147917 1.202027e+04 sprattus_sprattus_tot 0.00
#> 16 43.192661 1.295780e+02 amphipoda_tot 0.00
#> 17 43.192661 1.295780e+02 bivalvia_tot 0.00
#> 18 43.192661 1.295780e+02 clupeidae_tot 0.00
#> 19 43.192661 1.295780e+02 clupea_harengus_tot 0.00
#> 20 43.192661 1.295780e+02 gadus_morhua_tot 0.00
#> 21 43.192661 1.295780e+02 gobiidae_tot 0.00
#> 22 43.192661 1.295780e+02 mysidae_tot 0.01
#> 23 43.192661 1.295780e+02 non_bio_tot 0.00
#> 24 43.192661 1.295780e+02 other_crustacea_tot 0.00
#> 25 43.192661 1.295780e+02 other_tot 0.00
#> 26 43.192661 1.295780e+02 other_pisces_tot 0.00
#> 27 43.192661 1.295780e+02 platichthys_flesus_tot 0.00
#> 28 43.192661 1.295780e+02 polychaeta_tot 0.00
#> 29 43.192661 1.295780e+02 saduria_entomon_tot 0.00
#> 30 43.192661 1.295780e+02 sprattus_sprattus_tot 0.00
#> 31 0.000000 4.612069e-01 amphipoda_tot 0.00
#> 32 0.000000 4.612069e-01 bivalvia_tot 0.00
#> 33 0.000000 4.612069e-01 clupeidae_tot 0.00
#> 34 0.000000 4.612069e-01 clupea_harengus_tot 0.00
#> 35 0.000000 4.612069e-01 gadus_morhua_tot 0.00
#> 36 0.000000 4.612069e-01 gobiidae_tot 0.00
#> 37 0.000000 4.612069e-01 mysidae_tot 0.05
#> 38 0.000000 4.612069e-01 non_bio_tot 0.00
#> 39 0.000000 4.612069e-01 other_crustacea_tot 0.00
#> 40 0.000000 4.612069e-01 other_tot 0.00
#> 41 0.000000 4.612069e-01 other_pisces_tot 0.00
#> 42 0.000000 4.612069e-01 platichthys_flesus_tot 0.00
#> 43 0.000000 4.612069e-01 polychaeta_tot 0.00
#> 44 0.000000 4.612069e-01 saduria_entomon_tot 0.00
#> 45 0.000000 4.612069e-01 sprattus_sprattus_tot 0.00
#> 46 0.000000 4.612069e-01 amphipoda_tot 0.00
#> 47 0.000000 4.612069e-01 bivalvia_tot 0.00
#> 48 0.000000 4.612069e-01 clupeidae_tot 0.00
#> 49 0.000000 4.612069e-01 clupea_harengus_tot 0.00
#> 50 0.000000 4.612069e-01 gadus_morhua_tot 0.00
#> 51 0.000000 4.612069e-01 gobiidae_tot 0.00
#> 52 0.000000 4.612069e-01 mysidae_tot 0.00
#> 53 0.000000 4.612069e-01 non_bio_tot 0.00
#> 54 0.000000 4.612069e-01 other_crustacea_tot 0.00
#> 55 0.000000 4.612069e-01 other_tot 0.00
#> 56 0.000000 4.612069e-01 other_pisces_tot 0.00
#> 57 0.000000 4.612069e-01 platichthys_flesus_tot 0.00
#> 58 0.000000 4.612069e-01 polychaeta_tot 0.02
#> 59 0.000000 4.612069e-01 saduria_entomon_tot 0.00
#> 60 0.000000 4.612069e-01 sprattus_sprattus_tot 0.00
#> 61 4.447837 6.671756e+00 amphipoda_tot 0.00
#> 62 4.447837 6.671756e+00 bivalvia_tot 0.00
#> 63 4.447837 6.671756e+00 clupeidae_tot 0.00
#> 64 4.447837 6.671756e+00 clupea_harengus_tot 0.00
#> 65 4.447837 6.671756e+00 gadus_morhua_tot 0.00
#> 66 4.447837 6.671756e+00 gobiidae_tot 0.00
#> 67 4.447837 6.671756e+00 mysidae_tot 0.05
#> 68 4.447837 6.671756e+00 non_bio_tot 0.00
#> 69 4.447837 6.671756e+00 other_crustacea_tot 0.00
#> 70 4.447837 6.671756e+00 other_tot 0.00
#> 71 4.447837 6.671756e+00 other_pisces_tot 0.00
#> 72 4.447837 6.671756e+00 platichthys_flesus_tot 0.00
#> 73 4.447837 6.671756e+00 polychaeta_tot 0.00
#> 74 4.447837 6.671756e+00 saduria_entomon_tot 0.00
#> 75 4.447837 6.671756e+00 sprattus_sprattus_tot 0.00
#> 76 41903.529412 8.380706e+03 amphipoda_tot 0.00
#> 77 41903.529412 8.380706e+03 bivalvia_tot 0.00
#> 78 41903.529412 8.380706e+03 clupeidae_tot 0.00
#> 79 41903.529412 8.380706e+03 clupea_harengus_tot 0.00
#> 80 41903.529412 8.380706e+03 gadus_morhua_tot 0.00
#> 81 41903.529412 8.380706e+03 gobiidae_tot 0.00
#> 82 41903.529412 8.380706e+03 mysidae_tot 0.00
#> 83 41903.529412 8.380706e+03 non_bio_tot 0.00
#> 84 41903.529412 8.380706e+03 other_crustacea_tot 0.00
#> 85 41903.529412 8.380706e+03 other_tot 0.00
#> 86 41903.529412 8.380706e+03 other_pisces_tot 0.00
#> 87 41903.529412 8.380706e+03 platichthys_flesus_tot 0.00
#> 88 41903.529412 8.380706e+03 polychaeta_tot 0.02
#> 89 41903.529412 8.380706e+03 saduria_entomon_tot 0.00
#> 90 41903.529412 8.380706e+03 sprattus_sprattus_tot 0.00
#> 91 6429.360360 2.707099e+03 amphipoda_tot 0.00
#> 92 6429.360360 2.707099e+03 bivalvia_tot 0.00
#> 93 6429.360360 2.707099e+03 clupeidae_tot 0.00
#> 94 6429.360360 2.707099e+03 clupea_harengus_tot 0.00
#> 95 6429.360360 2.707099e+03 gadus_morhua_tot 0.00
#> 96 6429.360360 2.707099e+03 gobiidae_tot 0.00
#> 97 6429.360360 2.707099e+03 mysidae_tot 0.00
#> 98 6429.360360 2.707099e+03 non_bio_tot 0.00
#> 99 6429.360360 2.707099e+03 other_crustacea_tot 0.00
#> 100 6429.360360 2.707099e+03 other_tot 0.00
#> 101 6429.360360 2.707099e+03 other_pisces_tot 0.00
#> 102 6429.360360 2.707099e+03 platichthys_flesus_tot 0.00
#> 103 6429.360360 2.707099e+03 polychaeta_tot 0.00
#> 104 6429.360360 2.707099e+03 saduria_entomon_tot 0.00
#> 105 6429.360360 2.707099e+03 sprattus_sprattus_tot 0.00
#> 106 9580.918580 5.546848e+03 amphipoda_tot 0.00
#> 107 9580.918580 5.546848e+03 bivalvia_tot 0.00
#> 108 9580.918580 5.546848e+03 clupeidae_tot 0.00
#> 109 9580.918580 5.546848e+03 clupea_harengus_tot 0.00
#> 110 9580.918580 5.546848e+03 gadus_morhua_tot 0.00
#> 111 9580.918580 5.546848e+03 gobiidae_tot 0.00
#> 112 9580.918580 5.546848e+03 mysidae_tot 0.00
#> 113 9580.918580 5.546848e+03 non_bio_tot 0.00
#> 114 9580.918580 5.546848e+03 other_crustacea_tot 0.00
#> 115 9580.918580 5.546848e+03 other_tot 0.00
#> 116 9580.918580 5.546848e+03 other_pisces_tot 0.00
#> 117 9580.918580 5.546848e+03 platichthys_flesus_tot 0.00
#> 118 9580.918580 5.546848e+03 polychaeta_tot 0.00
#> 119 9580.918580 5.546848e+03 saduria_entomon_tot 0.00
#> 120 9580.918580 5.546848e+03 sprattus_sprattus_tot 0.00
#> 121 10913.343891 3.795946e+03 amphipoda_tot 0.00
#> 122 10913.343891 3.795946e+03 bivalvia_tot 0.00
#> 123 10913.343891 3.795946e+03 clupeidae_tot 0.00
#> 124 10913.343891 3.795946e+03 clupea_harengus_tot 0.00
#> 125 10913.343891 3.795946e+03 gadus_morhua_tot 0.00
#> 126 10913.343891 3.795946e+03 gobiidae_tot 0.00
#> 127 10913.343891 3.795946e+03 mysidae_tot 0.00
#> 128 10913.343891 3.795946e+03 non_bio_tot 0.00
#> 129 10913.343891 3.795946e+03 other_crustacea_tot 0.00
#> 130 10913.343891 3.795946e+03 other_tot 0.00
#> 131 10913.343891 3.795946e+03 other_pisces_tot 0.00
#> 132 10913.343891 3.795946e+03 platichthys_flesus_tot 0.00
#> 133 10913.343891 3.795946e+03 polychaeta_tot 0.01
#> 134 10913.343891 3.795946e+03 saduria_entomon_tot 0.00
#> 135 10913.343891 3.795946e+03 sprattus_sprattus_tot 0.00
#> 136 3745.626741 5.618440e+02 amphipoda_tot 0.00
#> 137 3745.626741 5.618440e+02 bivalvia_tot 0.00
#> 138 3745.626741 5.618440e+02 clupeidae_tot 0.00
#> 139 3745.626741 5.618440e+02 clupea_harengus_tot 0.00
#> 140 3745.626741 5.618440e+02 gadus_morhua_tot 0.00
#> 141 3745.626741 5.618440e+02 gobiidae_tot 0.00
#> 142 3745.626741 5.618440e+02 mysidae_tot 0.06
#> 143 3745.626741 5.618440e+02 non_bio_tot 0.00
#> 144 3745.626741 5.618440e+02 other_crustacea_tot 0.00
#> 145 3745.626741 5.618440e+02 other_tot 0.00
#> 146 3745.626741 5.618440e+02 other_pisces_tot 0.00
#> 147 3745.626741 5.618440e+02 platichthys_flesus_tot 0.00
#> 148 3745.626741 5.618440e+02 polychaeta_tot 0.00
#> 149 3745.626741 5.618440e+02 saduria_entomon_tot 0.00
#> 150 3745.626741 5.618440e+02 sprattus_sprattus_tot 0.00
#> 151 3898.476190 2.598984e+03 amphipoda_tot 0.00
#> 152 3898.476190 2.598984e+03 bivalvia_tot 0.00
#> 153 3898.476190 2.598984e+03 clupeidae_tot 0.00
#> 154 3898.476190 2.598984e+03 clupea_harengus_tot 0.00
#> 155 3898.476190 2.598984e+03 gadus_morhua_tot 0.00
#> 156 3898.476190 2.598984e+03 gobiidae_tot 0.00
#> 157 3898.476190 2.598984e+03 mysidae_tot 0.03
#> 158 3898.476190 2.598984e+03 non_bio_tot 0.00
#> 159 3898.476190 2.598984e+03 other_crustacea_tot 0.00
#> 160 3898.476190 2.598984e+03 other_tot 0.00
#> 161 3898.476190 2.598984e+03 other_pisces_tot 0.00
#> 162 3898.476190 2.598984e+03 platichthys_flesus_tot 0.00
#> 163 3898.476190 2.598984e+03 polychaeta_tot 0.00
#> 164 3898.476190 2.598984e+03 saduria_entomon_tot 0.00
#> 165 3898.476190 2.598984e+03 sprattus_sprattus_tot 0.00
#> 166 5357.123596 5.059506e+03 amphipoda_tot 0.00
#> 167 5357.123596 5.059506e+03 bivalvia_tot 0.00
#> 168 5357.123596 5.059506e+03 clupeidae_tot 0.00
#> 169 5357.123596 5.059506e+03 clupea_harengus_tot 0.00
#> 170 5357.123596 5.059506e+03 gadus_morhua_tot 0.00
#> 171 5357.123596 5.059506e+03 gobiidae_tot 0.00
#> 172 5357.123596 5.059506e+03 mysidae_tot 0.03
#> 173 5357.123596 5.059506e+03 non_bio_tot 0.00
#> 174 5357.123596 5.059506e+03 other_crustacea_tot 0.00
#> 175 5357.123596 5.059506e+03 other_tot 0.00
#> 176 5357.123596 5.059506e+03 other_pisces_tot 0.00
#> 177 5357.123596 5.059506e+03 platichthys_flesus_tot 0.00
#> 178 5357.123596 5.059506e+03 polychaeta_tot 0.00
#> 179 5357.123596 5.059506e+03 saduria_entomon_tot 0.00
#> 180 5357.123596 5.059506e+03 sprattus_sprattus_tot 0.00
#> 181 5888.715000 5.195925e+03 amphipoda_tot 0.00
#> 182 5888.715000 5.195925e+03 bivalvia_tot 0.00
#> 183 5888.715000 5.195925e+03 clupeidae_tot 0.00
#> 184 5888.715000 5.195925e+03 clupea_harengus_tot 0.00
#> 185 5888.715000 5.195925e+03 gadus_morhua_tot 0.00
#> 186 5888.715000 5.195925e+03 gobiidae_tot 0.00
#> 187 5888.715000 5.195925e+03 mysidae_tot 0.03
#> 188 5888.715000 5.195925e+03 non_bio_tot 0.00
#> 189 5888.715000 5.195925e+03 other_crustacea_tot 0.00
#> 190 5888.715000 5.195925e+03 other_tot 0.00
#> 191 5888.715000 5.195925e+03 other_pisces_tot 0.00
#> 192 5888.715000 5.195925e+03 platichthys_flesus_tot 0.00
#> 193 5888.715000 5.195925e+03 polychaeta_tot 0.00
#> 194 5888.715000 5.195925e+03 saduria_entomon_tot 0.00
#> 195 5888.715000 5.195925e+03 sprattus_sprattus_tot 0.00
#> 196 4357.403599 3.195429e+03 amphipoda_tot 0.00
#> 197 4357.403599 3.195429e+03 bivalvia_tot 0.00
#> 198 4357.403599 3.195429e+03 clupeidae_tot 0.00
#> 199 4357.403599 3.195429e+03 clupea_harengus_tot 0.00
#> 200 4357.403599 3.195429e+03 gadus_morhua_tot 0.00
#> 201 4357.403599 3.195429e+03 gobiidae_tot 0.00
#> 202 4357.403599 3.195429e+03 mysidae_tot 0.09
#> 203 4357.403599 3.195429e+03 non_bio_tot 0.00
#> 204 4357.403599 3.195429e+03 other_crustacea_tot 0.00
#> 205 4357.403599 3.195429e+03 other_tot 0.00
#> 206 4357.403599 3.195429e+03 other_pisces_tot 0.00
#> 207 4357.403599 3.195429e+03 platichthys_flesus_tot 0.00
#> 208 4357.403599 3.195429e+03 polychaeta_tot 0.01
#> 209 4357.403599 3.195429e+03 saduria_entomon_tot 0.00
#> 210 4357.403599 3.195429e+03 sprattus_sprattus_tot 0.00
#> 211 18.212291 1.365922e+01 amphipoda_tot 0.00
#> 212 18.212291 1.365922e+01 bivalvia_tot 0.00
#> 213 18.212291 1.365922e+01 clupeidae_tot 0.00
#> 214 18.212291 1.365922e+01 clupea_harengus_tot 0.00
#> 215 18.212291 1.365922e+01 gadus_morhua_tot 0.00
#> 216 18.212291 1.365922e+01 gobiidae_tot 0.00
#> 217 18.212291 1.365922e+01 mysidae_tot 0.11
#> 218 18.212291 1.365922e+01 non_bio_tot 0.00
#> 219 18.212291 1.365922e+01 other_crustacea_tot 0.00
#> 220 18.212291 1.365922e+01 other_tot 0.00
#> 221 18.212291 1.365922e+01 other_pisces_tot 0.00
#> 222 18.212291 1.365922e+01 platichthys_flesus_tot 0.00
#> 223 18.212291 1.365922e+01 polychaeta_tot 0.00
#> 224 18.212291 1.365922e+01 saduria_entomon_tot 0.00
#> 225 18.212291 1.365922e+01 sprattus_sprattus_tot 0.00
#> 226 43.933333 2.928889e+01 amphipoda_tot 0.00
#> 227 43.933333 2.928889e+01 bivalvia_tot 0.00
#> 228 43.933333 2.928889e+01 clupeidae_tot 0.00
#> 229 43.933333 2.928889e+01 clupea_harengus_tot 0.00
#> 230 43.933333 2.928889e+01 gadus_morhua_tot 0.00
#> 231 43.933333 2.928889e+01 gobiidae_tot 0.00
#> 232 43.933333 2.928889e+01 mysidae_tot 0.03
#> 233 43.933333 2.928889e+01 non_bio_tot 0.00
#> 234 43.933333 2.928889e+01 other_crustacea_tot 0.00
#> 235 43.933333 2.928889e+01 other_tot 0.00
#> 236 43.933333 2.928889e+01 other_pisces_tot 0.00
#> 237 43.933333 2.928889e+01 platichthys_flesus_tot 0.00
#> 238 43.933333 2.928889e+01 polychaeta_tot 0.00
#> 239 43.933333 2.928889e+01 saduria_entomon_tot 0.00
#> 240 43.933333 2.928889e+01 sprattus_sprattus_tot 0.00
#> 241 26.078212 6.519553e+01 amphipoda_tot 0.00
#> 242 26.078212 6.519553e+01 bivalvia_tot 0.00
#> 243 26.078212 6.519553e+01 clupeidae_tot 0.00
#> 244 26.078212 6.519553e+01 clupea_harengus_tot 0.00
#> 245 26.078212 6.519553e+01 gadus_morhua_tot 0.00
#> 246 26.078212 6.519553e+01 gobiidae_tot 0.00
#> 247 26.078212 6.519553e+01 mysidae_tot 0.00
#> 248 26.078212 6.519553e+01 non_bio_tot 0.00
#> 249 26.078212 6.519553e+01 other_crustacea_tot 0.01
#> 250 26.078212 6.519553e+01 other_tot 0.00
#> 251 26.078212 6.519553e+01 other_pisces_tot 0.00
#> 252 26.078212 6.519553e+01 platichthys_flesus_tot 0.00
#> 253 26.078212 6.519553e+01 polychaeta_tot 0.00
#> 254 26.078212 6.519553e+01 saduria_entomon_tot 0.00
#> 255 26.078212 6.519553e+01 sprattus_sprattus_tot 0.00
#> 256 5249.883249 4.420954e+03 amphipoda_tot 0.00
#> 257 5249.883249 4.420954e+03 bivalvia_tot 0.00
#> 258 5249.883249 4.420954e+03 clupeidae_tot 0.00
#> 259 5249.883249 4.420954e+03 clupea_harengus_tot 0.00
#> 260 5249.883249 4.420954e+03 gadus_morhua_tot 0.00
#> 261 5249.883249 4.420954e+03 gobiidae_tot 0.00
#> 262 5249.883249 4.420954e+03 mysidae_tot 0.05
#> 263 5249.883249 4.420954e+03 non_bio_tot 0.00
#> 264 5249.883249 4.420954e+03 other_crustacea_tot 0.00
#> 265 5249.883249 4.420954e+03 other_tot 0.00
#> 266 5249.883249 4.420954e+03 other_pisces_tot 0.00
#> 267 5249.883249 4.420954e+03 platichthys_flesus_tot 0.00
#> 268 5249.883249 4.420954e+03 polychaeta_tot 0.00
#> 269 5249.883249 4.420954e+03 saduria_entomon_tot 0.00
#> 270 5249.883249 4.420954e+03 sprattus_sprattus_tot 0.00
#> 271 7913.255814 6.782791e+03 amphipoda_tot 0.00
#> 272 7913.255814 6.782791e+03 bivalvia_tot 0.00
#> 273 7913.255814 6.782791e+03 clupeidae_tot 0.00
#> 274 7913.255814 6.782791e+03 clupea_harengus_tot 0.00
#> 275 7913.255814 6.782791e+03 gadus_morhua_tot 0.00
#> 276 7913.255814 6.782791e+03 gobiidae_tot 0.00
#> 277 7913.255814 6.782791e+03 mysidae_tot 0.01
#> 278 7913.255814 6.782791e+03 non_bio_tot 0.00
#> 279 7913.255814 6.782791e+03 other_crustacea_tot 0.00
#> 280 7913.255814 6.782791e+03 other_tot 0.00
#> 281 7913.255814 6.782791e+03 other_pisces_tot 0.00
#> 282 7913.255814 6.782791e+03 platichthys_flesus_tot 0.00
#> 283 7913.255814 6.782791e+03 polychaeta_tot 0.00
#> 284 7913.255814 6.782791e+03 saduria_entomon_tot 0.00
#> 285 7913.255814 6.782791e+03 sprattus_sprattus_tot 0.00
#> 286 4007.854545 2.504909e+03 amphipoda_tot 0.00
#> 287 4007.854545 2.504909e+03 bivalvia_tot 0.00
#> 288 4007.854545 2.504909e+03 clupeidae_tot 0.00
#> 289 4007.854545 2.504909e+03 clupea_harengus_tot 0.00
#> 290 4007.854545 2.504909e+03 gadus_morhua_tot 0.00
#> 291 4007.854545 2.504909e+03 gobiidae_tot 0.00
#> 292 4007.854545 2.504909e+03 mysidae_tot 0.02
#> 293 4007.854545 2.504909e+03 non_bio_tot 0.00
#> 294 4007.854545 2.504909e+03 other_crustacea_tot 0.00
#> 295 4007.854545 2.504909e+03 other_tot 0.00
#> 296 4007.854545 2.504909e+03 other_pisces_tot 0.00
#> 297 4007.854545 2.504909e+03 platichthys_flesus_tot 0.00
#> 298 4007.854545 2.504909e+03 polychaeta_tot 0.00
#> 299 4007.854545 2.504909e+03 saduria_entomon_tot 0.00
#> 300 4007.854545 2.504909e+03 sprattus_sprattus_tot 0.00
#> 301 11382.588727 7.398683e+03 amphipoda_tot 0.00
#> 302 11382.588727 7.398683e+03 bivalvia_tot 0.00
#> 303 11382.588727 7.398683e+03 clupeidae_tot 0.00
#> 304 11382.588727 7.398683e+03 clupea_harengus_tot 0.00
#> 305 11382.588727 7.398683e+03 gadus_morhua_tot 0.00
#> 306 11382.588727 7.398683e+03 gobiidae_tot 0.00
#> 307 11382.588727 7.398683e+03 mysidae_tot 0.00
#> 308 11382.588727 7.398683e+03 non_bio_tot 0.00
#> 309 11382.588727 7.398683e+03 other_crustacea_tot 0.00
#> 310 11382.588727 7.398683e+03 other_tot 0.00
#> 311 11382.588727 7.398683e+03 other_pisces_tot 0.00
#> 312 11382.588727 7.398683e+03 platichthys_flesus_tot 0.00
#> 313 11382.588727 7.398683e+03 polychaeta_tot 0.01
#> 314 11382.588727 7.398683e+03 saduria_entomon_tot 0.00
#> 315 11382.588727 7.398683e+03 sprattus_sprattus_tot 0.00
#> 316 9313.785867 3.449550e+03 amphipoda_tot 0.00
#> 317 9313.785867 3.449550e+03 bivalvia_tot 0.00
#> 318 9313.785867 3.449550e+03 clupeidae_tot 0.00
#> 319 9313.785867 3.449550e+03 clupea_harengus_tot 0.00
#> 320 9313.785867 3.449550e+03 gadus_morhua_tot 0.00
#> 321 9313.785867 3.449550e+03 gobiidae_tot 0.00
#> 322 9313.785867 3.449550e+03 mysidae_tot 0.00
#> 323 9313.785867 3.449550e+03 non_bio_tot 0.00
#> 324 9313.785867 3.449550e+03 other_crustacea_tot 0.01
#> 325 9313.785867 3.449550e+03 other_tot 0.00
#> 326 9313.785867 3.449550e+03 other_pisces_tot 0.00
#> 327 9313.785867 3.449550e+03 platichthys_flesus_tot 0.00
#> 328 9313.785867 3.449550e+03 polychaeta_tot 0.02
#> 329 9313.785867 3.449550e+03 saduria_entomon_tot 0.00
#> 330 9313.785867 3.449550e+03 sprattus_sprattus_tot 0.00
max_size_cod <- 65
cod_important_prey3 <- long_cod %>%
mutate(pred_length_cm2 = ifelse(pred_length_cm > max_size_cod, max_size_cod -1, pred_length_cm)) %>%
mutate(predator_length_grp = cut(pred_length_cm2, breaks = seq(0, 100, by = 5))) %>%
group_by(prey_group, predator_length_grp) %>%
summarise(prey_group_tot = sum(tot_prey_weight)) %>%
ungroup() %>%
group_by(predator_length_grp) %>%
mutate(prop = prey_group_tot / sum(prey_group_tot)) %>%
ungroup() %>%
mutate(max_size = as.numeric(substr(predator_length_grp, 5, 6)),
max_size = ifelse(predator_length_grp == "(0,5]", 5, max_size),
max_size = ifelse(predator_length_grp == "(5,10]", 10, max_size),
predator = "Cod")
#> mutate: new variable 'pred_length_cm2' (double) with 62 unique values and 0% NA
#> mutate: new variable 'predator_length_grp' (factor) with 13 unique values and 0% NA
#> group_by: 2 grouping variables (prey_group, predator_length_grp)
#> summarise: now 195 rows and 3 columns, one group variable remaining (prey_group)
#> ungroup: no grouping variables
#> group_by: one grouping variable (predator_length_grp)
#> mutate (grouped): new variable 'prop' (double) with 144 unique values and 0% NA
#> ungroup: no grouping variables
#> Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
#> mutate: new variable 'max_size' (double) with 13 unique values and 0% NA
#> new variable 'predator' (character) with one unique value and 0% NA
max_size_fle = 40
fle_important_prey3 <- long_fle %>%
mutate(pred_length_cm2 = ifelse(pred_length_cm > max_size_fle, max_size_fle-1, pred_length_cm)) %>%
mutate(predator_length_grp = cut(pred_length_cm2, breaks = seq(0, 100, by = 5))) %>%
group_by(prey_group, predator_length_grp) %>%
summarise(prey_group_tot = sum(tot_prey_weight)) %>%
ungroup() %>%
group_by(predator_length_grp) %>%
mutate(prop = prey_group_tot / sum(prey_group_tot)) %>%
ungroup() %>%
mutate(max_size = as.numeric(substr(predator_length_grp, 5, 6)),
max_size = ifelse(predator_length_grp == "(0,5]", 5, max_size),
max_size = ifelse(predator_length_grp == "(5,10]", 10, max_size),
predator = "Flounder")
#> mutate: new variable 'pred_length_cm2' (double) with 31 unique values and 0% NA
#> mutate: new variable 'predator_length_grp' (factor) with 7 unique values and 0% NA
#> group_by: 2 grouping variables (prey_group, predator_length_grp)
#> summarise: now 105 rows and 3 columns, one group variable remaining (prey_group)
#> ungroup: no grouping variables
#> group_by: one grouping variable (predator_length_grp)
#> mutate (grouped): new variable 'prop' (double) with 70 unique values and 0% NA
#> ungroup: no grouping variables
#> Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
#> mutate: new variable 'max_size' (double) with 7 unique values and 0% NA
#> new variable 'predator' (character) with one unique value and 0% NA
area_plot <- bind_rows(fle_important_prey3, cod_important_prey3) %>%
mutate(prop = replace_na(prop, 0))
#> mutate: no changes
n_cat <- nrow(area_plot %>% distinct(prey_group))
#> distinct: removed 285 rows (95%), 15 rows remaining
colourCount <- n_cat
getPalette <- colorRampPalette(brewer.pal(12, "Paired"))
pal <- getPalette(colourCount)
area_plot %>% distinct(prey_group)
#> distinct: removed 285 rows (95%), 15 rows remaining
#> # A tibble: 15 × 1
#> prey_group
#> <chr>
#> 1 amphipoda_tot
#> 2 bivalvia_tot
#> 3 clupea_harengus_tot
#> 4 clupeidae_tot
#> 5 gadus_morhua_tot
#> 6 gobiidae_tot
#> 7 mysidae_tot
#> 8 non_bio_tot
#> 9 other_crustacea_tot
#> 10 other_pisces_tot
#> 11 other_tot
#> 12 platichthys_flesus_tot
#> 13 polychaeta_tot
#> 14 saduria_entomon_tot
#> 15 sprattus_sprattus_tot
area_plot <- area_plot %>%
mutate(prey_group = ifelse(prey_group == "amphipoda_tot", "Amphipoda", prey_group),
prey_group = ifelse(prey_group == "bivalvia_tot", "Bivalvia", prey_group),
prey_group = ifelse(prey_group == "clupea_harengus_tot", "Clupea harengus", prey_group),
prey_group = ifelse(prey_group == "clupeidae_tot", "Clupeidae", prey_group),
prey_group = ifelse(prey_group == "gadus_morhua_tot", "Gadus morhua", prey_group),
prey_group = ifelse(prey_group == "gobiidae_tot", "Gobiidae", prey_group),
prey_group = ifelse(prey_group == "mysidae_tot", "Mysidae", prey_group),
prey_group = ifelse(prey_group == "non_bio_tot", "Non-bio", prey_group),
prey_group = ifelse(prey_group == "other_crustacea_tot", "Other crustacea", prey_group),
prey_group = ifelse(prey_group == "other_pisces_tot", "Other pisces", prey_group),
prey_group = ifelse(prey_group == "other_tot", "Other", prey_group),
prey_group = ifelse(prey_group == "platichthys_flesus_tot", "Platichthys flesus", prey_group),
prey_group = ifelse(prey_group == "polychaeta_tot", "Polychaeta", prey_group),
prey_group = ifelse(prey_group == "saduria_entomon_tot", "Saduria entomon", prey_group),
prey_group = ifelse(prey_group == "sprattus_sprattus_tot", "Sprattus sprattus", prey_group))
#> mutate: changed 300 values (100%) of 'prey_group' (0 new NA)
# fill_order <- factor(area_plot$prey_group,
# levels = c("Sprattus sprattus", "Clupea harengus", "Clupeidae",
# "Gobiidae", "Other pisces", "Gadiformes", "Gadus morhua",
# "Platichthys flesus", "Amphipoda", "Bivalvia", "Mysidae",
# "Polychaeta", "Saduria entomon", "Other crustacea",
# "Other", "Non-bio"))
# Dataframes for geom_text with sample size
n_cod <- long_cod %>%
mutate(pred_length_cm2 = ifelse(pred_length_cm > max_size_cod, max_size_cod -1, pred_length_cm)) %>%
mutate(predator_length_grp = cut(pred_length_cm2, breaks = seq(0, 100, by = 5))) %>%
group_by(predator_length_grp) %>%
summarise(n = length(unique(pred_id)))
#> mutate: new variable 'pred_length_cm2' (double) with 62 unique values and 0% NA
#> mutate: new variable 'predator_length_grp' (factor) with 13 unique values and 0% NA
#> group_by: one grouping variable (predator_length_grp)
#> summarise: now 13 rows and 2 columns, ungrouped
n_fle <- long_fle %>%
mutate(pred_length_cm2 = ifelse(pred_length_cm > max_size_fle, max_size_fle-1, pred_length_cm)) %>%
mutate(predator_length_grp = cut(pred_length_cm2, breaks = seq(0, 100, by = 5))) %>%
group_by(predator_length_grp) %>%
summarise(n = length(unique(pred_id)))
#> mutate: new variable 'pred_length_cm2' (double) with 31 unique values and 0% NA
#> mutate: new variable 'predator_length_grp' (factor) with 7 unique values and 0% NA
#> group_by: one grouping variable (predator_length_grp)
#> summarise: now 7 rows and 2 columns, ungrouped
n_dat <- bind_rows(n_cod %>% mutate(predator = "Cod"),
n_fle %>% mutate(predator = "Flounder")) %>%
mutate(max_size = as.numeric(substr(predator_length_grp, 5, 6)),
max_size = ifelse(predator_length_grp == "(0,5]", 5, max_size),
max_size = ifelse(predator_length_grp == "(5,10]", 10, max_size))
#> mutate: new variable 'predator' (character) with one unique value and 0% NA
#> mutate: new variable 'predator' (character) with one unique value and 0% NA
#> Warning in mask$eval_all_mutate(quo): NAs introduced by coercion
#> mutate: new variable 'max_size' (double) with 13 unique values and 0% NA
#ggplot(data = area_plot, aes(x = min_size, y = prop, fill = fill_order, color = fill_order)) +
ggplot(data = area_plot, aes(x = max_size, y = prop, fill = prey_group, color = prey_group)) +
geom_col(width = 4.3) +
geom_text(data = n_dat, aes(x = max_size, y = 1.038, label = n), inherit.aes = FALSE,
size = 0, color = "white") +
geom_text(data = n_dat, aes(x = max_size, y = 1.02, label = n), inherit.aes = FALSE,
size = 2) +
facet_wrap(~predator, scales = "free") +
scale_fill_manual(values = pal, name = "") +
scale_color_manual(values = pal, name = "") +
coord_cartesian(expand = 0) +
scale_x_continuous(breaks = seq(0, 100, 5)) +
labs(y = "Proportion", x = "Max. predator size in group [cm]") +
theme(legend.position = "bottom",
aspect.ratio = 1) +
NULL
ggsave("figures/ontogenetic_diet.pdf", width = 17, height = 17, units = "cm")Calculate response variables
# Total feeding ratio, proportion of saduria and proportion of common prey!
# Cod
wide_cod <- d_wide_cod %>%
drop_na(pred_weight_g) %>%
mutate(tot_prey_biom = amphipoda_tot + bivalvia_tot + clupeidae_tot + clupea_harengus_tot +
gadus_morhua_tot + gobiidae_tot + mysidae_tot + non_bio_tot +
other_crustacea_tot + other_tot + other_pisces_tot + platichthys_flesus_tot +
polychaeta_tot + saduria_entomon_tot + sprattus_sprattus_tot,
tot_benthic_prey_biom = amphipoda_tot + bivalvia_tot + gadus_morhua_tot +
gobiidae_tot + mysidae_tot + non_bio_tot +
other_crustacea_tot + other_tot + other_pisces_tot + platichthys_flesus_tot +
polychaeta_tot + saduria_entomon_tot,
tot_sprat_biom = sprattus_sprattus_tot,
tot_herring_biom = clupea_harengus_tot,
tot_pelagic_biom = clupeidae_tot + clupea_harengus_tot + sprattus_sprattus_tot,
tot_common_prey_biom = other_crustacea_tot + other_pisces_tot + polychaeta_tot +
saduria_entomon_tot + sprattus_sprattus_tot,
tot_feeding_ratio = (tot_prey_biom)/(pred_weight_g - tot_prey_biom),
benthic_feeding_ratio = (tot_benthic_prey_biom)/(pred_weight_g - tot_prey_biom),
sprat_feeding_ratio = tot_sprat_biom/(pred_weight_g - tot_pelagic_biom),
herring_feeding_ratio = tot_herring_biom/(pred_weight_g - tot_pelagic_biom),
pelagic_feeding_ratio = tot_pelagic_biom/(pred_weight_g - tot_pelagic_biom),
common_feeding_ratio = (tot_common_prey_biom)/(pred_weight_g - tot_prey_biom),
saduria_feeding_ratio = (saduria_entomon_tot)/(pred_weight_g - tot_prey_biom)) %>%
filter(tot_feeding_ratio < 0.4) %>% # Seems like a reasonable cutoff
dplyr::select(-amphipoda_tot, -bivalvia_tot, -clupeidae_tot, -clupea_harengus_tot,
-gadus_morhua_tot, -gobiidae_tot, -mysidae_tot, -non_bio_tot,
-other_crustacea_tot, -other_tot, -other_pisces_tot, -platichthys_flesus_tot,
-polychaeta_tot, -saduria_entomon_tot, -sprattus_sprattus_tot,
-tot_prey_biom, -tot_benthic_prey_biom, -tot_common_prey_biom,
-tot_pelagic_biom, -tot_sprat_biom, -tot_herring_biom)
#> drop_na: no rows removed
#> mutate: new variable 'tot_prey_biom' (double) with 1,390 unique values and 0% NA
#> new variable 'tot_benthic_prey_biom' (double) with 773 unique values and 0% NA
#> new variable 'tot_sprat_biom' (double) with 492 unique values and 0% NA
#> new variable 'tot_herring_biom' (double) with 281 unique values and 0% NA
#> new variable 'tot_pelagic_biom' (double) with 772 unique values and 0% NA
#> new variable 'tot_common_prey_biom' (double) with 847 unique values and 0% NA
#> new variable 'tot_feeding_ratio' (double) with 2,475 unique values and 0% NA
#> new variable 'benthic_feeding_ratio' (double) with 1,988 unique values and 0% NA
#> new variable 'sprat_feeding_ratio' (double) with 555 unique values and 0% NA
#> new variable 'herring_feeding_ratio' (double) with 294 unique values and 0% NA
#> new variable 'pelagic_feeding_ratio' (double) with 890 unique values and 0% NA
#> new variable 'common_feeding_ratio' (double) with 1,814 unique values and 0% NA
#> new variable 'saduria_feeding_ratio' (double) with 352 unique values and 0% NA
#> filter: removed 4 rows (<1%), 3,306 rows remaining
# Flounder
wide_fle <- d_wide_fle %>%
drop_na(pred_weight_g) %>%
mutate(tot_prey_biom = amphipoda_tot + bivalvia_tot + clupeidae_tot + clupea_harengus_tot +
gadus_morhua_tot + gobiidae_tot + mysidae_tot + non_bio_tot +
other_crustacea_tot + other_tot + other_pisces_tot + platichthys_flesus_tot +
polychaeta_tot + saduria_entomon_tot + sprattus_sprattus_tot,
tot_benthic_prey_biom = amphipoda_tot + bivalvia_tot + gadus_morhua_tot +
gobiidae_tot + mysidae_tot + non_bio_tot +
other_crustacea_tot + other_tot + other_pisces_tot + platichthys_flesus_tot +
polychaeta_tot + saduria_entomon_tot,
tot_sprat_biom = sprattus_sprattus_tot,
tot_herring_biom = clupea_harengus_tot,
tot_pelagic_biom = clupeidae_tot + clupea_harengus_tot + sprattus_sprattus_tot,
tot_common_prey_biom = other_crustacea_tot + other_pisces_tot + polychaeta_tot +
saduria_entomon_tot + sprattus_sprattus_tot,
tot_feeding_ratio = (tot_prey_biom)/(pred_weight_g - tot_prey_biom),
benthic_feeding_ratio = (tot_benthic_prey_biom)/(pred_weight_g - tot_prey_biom),
sprat_feeding_ratio = tot_sprat_biom/(pred_weight_g - tot_pelagic_biom),
herring_feeding_ratio = tot_herring_biom/(pred_weight_g - tot_pelagic_biom),
pelagic_feeding_ratio = tot_pelagic_biom/(pred_weight_g - tot_pelagic_biom),
common_feeding_ratio = (tot_common_prey_biom)/(pred_weight_g - tot_prey_biom),
saduria_feeding_ratio = (saduria_entomon_tot)/(pred_weight_g - tot_prey_biom)) %>%
filter(tot_feeding_ratio < 0.4) %>% # Seems like a reasonable cutoff
dplyr::select(-amphipoda_tot, -bivalvia_tot, -clupeidae_tot, -clupea_harengus_tot,
-gadus_morhua_tot, -gobiidae_tot, -mysidae_tot, -non_bio_tot,
-other_crustacea_tot, -other_tot, -other_pisces_tot, -platichthys_flesus_tot,
-polychaeta_tot, -saduria_entomon_tot, -sprattus_sprattus_tot,
-tot_prey_biom, -tot_benthic_prey_biom, -tot_common_prey_biom,
-tot_pelagic_biom, -tot_sprat_biom, -tot_herring_biom)
#> drop_na: no rows removed
#> mutate: new variable 'tot_prey_biom' (double) with 1,168 unique values and 0% NA
#> new variable 'tot_benthic_prey_biom' (double) with 1,147 unique values and 0% NA
#> new variable 'tot_sprat_biom' (double) with 35 unique values and 0% NA
#> new variable 'tot_herring_biom' (double) with 7 unique values and 0% NA
#> new variable 'tot_pelagic_biom' (double) with 44 unique values and 0% NA
#> new variable 'tot_common_prey_biom' (double) with 657 unique values and 0% NA
#> new variable 'tot_feeding_ratio' (double) with 1,635 unique values and 0% NA
#> new variable 'benthic_feeding_ratio' (double) with 1,609 unique values and 0% NA
#> new variable 'sprat_feeding_ratio' (double) with 36 unique values and 0% NA
#> new variable 'herring_feeding_ratio' (double) with 7 unique values and 0% NA
#> new variable 'pelagic_feeding_ratio' (double) with 47 unique values and 0% NA
#> new variable 'common_feeding_ratio' (double) with 1,260 unique values and 0% NA
#> new variable 'saduria_feeding_ratio' (double) with 596 unique values and 0% NA
#> filter: no rows removed
# Plot tot_feeding_ratio for all years
ggplot(wide_cod, aes(year, tot_feeding_ratio)) +
geom_jitter(size = 3, shape = 21, color = "white", fill = "gray30") +
stat_smooth(method = "gam", formula = y~s(x, k = 3)) +
facet_wrap(~quarter, ncol = 1, scales = "free") +
NULL
ggplot(wide_fle, aes(year, tot_feeding_ratio)) +
geom_jitter(size = 3, shape = 21, color = "white", fill = "gray30") +
stat_smooth(method = "gam", formula = y~s(x, k = 3)) +
facet_wrap(~quarter, ncol = 1, scales = "free") +
NULL
dat <- bind_rows(wide_fle, wide_cod)
glimpse(dat)
#> Rows: 5,885
#> Columns: 29
#> $ pred_id <chr> "2015_4_FLE_1", "2015_4_FLE_10", "2015_4_FLE_125…
#> $ predator_latin_name <chr> "Platichthys flesus", "Platichthys flesus", "Pla…
#> $ species <chr> "Flounder", "Flounder", "Flounder", "Flounder", …
#> $ pred_weight_g <dbl> 219.52, 175.76, 593.19, 428.75, 297.91, 219.52, …
#> $ pred_length_cm <dbl> 28, 26, 39, 35, 31, 28, 27, 32, 20, 21, 24, 31, …
#> $ year <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, …
#> $ quarter <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, …
#> $ month <dbl> 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, …
#> $ day <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ ices_rect <chr> "40G4", "40G4", "40G4", "40G4", "40G4", "41G7", …
#> $ subdiv <dbl> 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, …
#> $ haul_id <chr> "2015_4_2", "2015_4_2", "2015_4_6", "2015_4_6", …
#> $ X <dbl> 474.8173, 474.8173, 460.1953, 460.1953, 474.8173…
#> $ Y <dbl> 6165.344, 6165.344, 6172.873, 6172.873, 6165.344…
#> $ lat <dbl> 55.63333, 55.63333, 55.70000, 55.70000, 55.63333…
#> $ lon <dbl> 14.60000, 14.60000, 14.36667, 14.36667, 14.60000…
#> $ depth <dbl> 60.2, 60.2, 37.9, 37.9, 60.2, 41.6, 41.6, 41.6, …
#> $ pred_weight_source <chr> "estimated_from_length", "estimated_from_length"…
#> $ cruise <chr> "BITS", "BITS", "BITS", "BITS", "BITS", "BITS", …
#> $ fle_kg_km2 <dbl> 330.2812, 330.2812, 2226.6515, 2226.6515, 330.28…
#> $ lcod_kg_km2 <dbl> 41403.148, 41403.148, 12382.688, 12382.688, 4140…
#> $ scod_kg_km2 <dbl> 12020.269, 12020.269, 1547.836, 1547.836, 12020.…
#> $ tot_feeding_ratio <dbl> 0.0039330467, 0.0032536104, 0.0147806005, 0.0417…
#> $ benthic_feeding_ratio <dbl> 0.0039330467, 0.0032536104, 0.0147806005, 0.0417…
#> $ sprat_feeding_ratio <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ herring_feeding_ratio <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ pelagic_feeding_ratio <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ common_feeding_ratio <dbl> 0.0000914662, 0.0022832353, 0.0000000000, 0.0000…
#> $ saduria_feeding_ratio <dbl> 0.0000000000, 0.0000000000, 0.0000000000, 0.0000…
nrow(dat)
#> [1] 5885
length(unique(dat$pred_id))
#> [1] 5885write_csv(dat, "data/clean/clean_stomach_data.csv")